Sepsis-induced acute liver injury often develops in the early stages of sepsis and can exacerbate the pathology by contributing to multiple organ dysfunction and increasing lethality. No specific therapies for sepsis-induced liver injury are currently available; therefore, effective countermeasures are urgently needed. Considering the crucial role of neutrophils in sepsis-induced liver injury, herein, neutrophil membrane-mimicking nanodecoys (NM) were explored as a biomimetic nanomedicine for the treatment of sepsis-associated liver injury. NM administration exhibited excellent biocompatibility and dramatically decreased the plasma levels of inflammatory cytokines and liver injury biomarkers, including aspartate aminotransferase, alanine aminotransferase, and direct bilirubin, in a sepsis mouse model. NM treatment also reduced hepatic malondialdehyde content, myeloperoxidase activity, and histological injury, and ultimately improved survival in the septic mice. Further in vitro studies showed that NM treatment neutralized the neutrophil chemokines and inflammatory mediators and directly mitigated neutrophil chemotaxis and adhesion. Additionally, NM also markedly weakened lipopolysaccharide-induced reactive oxygen species generation, cyclooxygenase-2 expression, nitric oxide secretion, and subsequent hepatocyte injury. Thus, this study provides a promising therapeutic strategy for the management of sepsis-induced acute liver injury.We present the case of an elderly female who underwent a workup for acute blood loss anemia that incidentally led to the discovery of abdominopelvic actinomycosis. While esophagogastroduodenoscopy and colonoscopy were unremarkable, CT abdomen/pelvis displayed a soft tissue mass in the left sacral ala and presacral area that appeared suspicious for malignancy. MRI pelvis revealed a presacral abscess, and an IR-guided biopsy cemented the diagnosis. This case exemplifies how actinomycosis can mimic the presentation of cancer. Risk factors included a history of ischemic colitis and lumbar laminectomy, as mucosal tissue compromise and orthopedic hardware can be niduses for infection.This paper proposes an e-diagnosis system based on machine learning (ML) algorithms to be implemented on the Internet of Medical Things (IoMT) environment, particularly for diagnosing diabetes mellitus (type 2 diabetes). However, the ML applications tend to be mistrusted because of their inability to show the internal decision-making process, resulting in slow uptake by end-users within certain healthcare sectors. This research delineates the use of three interpretable supervised ML models Naïve Bayes classifier, random forest classifier, and J48 decision tree models to be trained and tested using the Pima Indians diabetes dataset in R programming language. The performance of each algorithm is analyzed to determine the one with the best accuracy, precision, sensitivity, and specificity. An assessment of the decision process is also made to improve the model. It can be concluded that a Naïve Bayes model works well with a more fine-tuned selection of features for binary classification, while random forest works better with more features.Financial time series are chaotic that, in turn, leads their predictability to be complex and challenging. This paper presents a novel financial time series prediction hybrid that involves Chaos Theory, Convolutional neural network (CNN), and Polynomial Regression (PR). The financial time series is first checked in this hybrid for the presence of chaos. The chaos in the series of times is later modeled using Chaos Theory. The modeled time series is input to CNN to obtain initial predictions. The error series obtained from CNN predictions is fit by PR to get error predictions. The error predictions and initial predictions from CNN are added to obtain the final predictions of the hybrid model. The effectiveness of the proposed hybrid (Chaos+CNN+PR) is tested by using three types of Foreign exchange rates of financial time series (INR/USD, JPY/USD, SGD/USD), commodity prices (Gold, Crude Oil, Soya beans), and stock market indices (S&P 500, Nifty 50, Shanghai Composite). The proposed hybrid is superior to Auto-regressive integrated moving averages (ARIMA), Prophet, Classification and Regression Tree (CART), Random Forest (RF), CNN, Chaos+CART, Chaos+RF and Chaos+CNN in terms of MSE, MAPE, Dstat, and Theil’s U.

Acute appendicitis cases increased in severity following COVID-19-related restrictions in March, 2020. We investigated if similar changes occurred during Wave 2.

Acute appendicitis patients during Wave 1 were grouped 8 weeks before (Group A) and after (Group B) stay-at-home restrictions were initiated on March 15, 2020. Cases in Wave 2 were grouped 8 weeks before (Group C) and after (Group D) November 6, 2020. Groups were compared to equivalent time frames in 2018/2019.

Group A versus B revealed 42.6% decrease (confidence interval -59.4 to -25.7) in uncomplicated appendicitis and 21.1% increase (confidence interval 4.8-37.3) in perforated appendicitis. Similar patterns were noted comparing Group C versus D without statistical significance. The changes seen in Wave 1 were significantly different than in 2018/2019. This trend continued in Wave 2.

Similar to Wave 1, acute appendicitis cases increased in severity during wave 2 of COVID-19, but with less prominence.

Similar to Wave 1, acute appendicitis cases increased in severity during wave 2 of COVID-19, but with less prominence.

New predictors of the efficacy of hepatocellular carcinoma (HCC) immunotherapy are needed. The ability of a single gene mutation to predict the therapeutic effect of immune checkpoint inhibitors (ICI) in HCC remains unknown.

The most frequently mutated genes in HCC were analyzed using the Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) datasets. Mutant genes that correlated with the tumor mutational burden (TMB) and prognosis were obtained. The mutation pattern and immunological function of one of the most frequently mutated genes, LRP1B, were determined. A pan-tumor analysis of LRP1B expression, association with cancer prognosis, and immunological role was also explored. A retrospective clinical study was conducted using 102 HCC patients who received ICI treatment to further verify whether gene mutations can predict the effectiveness of immunotherapy and prognosis of HCC.

LRP1B is among the most frequently mutated genes in HCC cohorts in TCGA and ICGC datasets. TCGA data shts. HighLRP1B expression correlated with tumor immunity and HCC prognosis.

To describe the lived experience of healthcare staff during the Coronavirus Disease 2019 (COVID-19) pandemic relating to the use of personal protective equipment (PPE) and investigate risks associated with PPE use, error mitigation and acceptability of mindfulness incorporation into PPE practice.

A qualitative human factors’ study at two Irish hospitals occurred in late 2020. Data was collected by semi-structured interview and included role description, pre-COVID-19 PPE experience, the impact of COVID-19 on lived experience, risks associated with PPE use, contributory factors to errors, error mitigation strategies and acceptability of incorporating mindfulness into PPE practice.

Of 45 participants, 23 of whom were nursing staff (51%), 34 (76%) had previously worn PPE and 25 (56%) used a buddy system. COVID-19 lived experience impacted most on social life/home-work interface (n=36, 80%). Nineteen staff (42%) described mental health impacts. The most cited risk concerned 'knowledge of procedures’ (n=18, 40%). Contributory factors to PPE errors included time (n=15, 43%) and staffing pressures (n=10, 29%). Mitigation interventions included training/education (n=12, 40%). The majority (n=35, 78%) supported mindfulness integration into PPE practice.

PPE training should address healthcare staff lived experiences and consider incorporation of mindfulness and key organisational factors contributing to safety.

PPE training should address healthcare staff lived experiences and consider incorporation of mindfulness and key organisational factors contributing to safety.Soon after the onset of the COVID-19 pandemic, the French government decided to still hold the first round of the 2020 municipal elections as scheduled on March 15. What was the impact of these elections on the spread of COVID-19 in France? Answering this question leads to intricate econometric issues as omitted variables may drive both epidemiological dynamics and electoral turnout, and as a national lockdown was imposed at almost the same time as the elections. In order to disentangle the effect of the elections from that of confounding factors, we first predict each department’s epidemiological dynamics using information up to the election. We then take advantage of differences in electoral turnout across departments to identify the impact of the election on prediction errors in hospitalizations. We report a detrimental effect of the first round of the election on hospitalizations in locations that were already at relatively advanced stages of the epidemic. Estimates suggest that the elections accounted for at least 3,000 hospitalizations, or 11% of all hospitalizations by the end of March. Given the sizable health cost of holding elections during an epidemic, promoting ways of voting that reduce exposure to COVID-19 is key until the pandemic shows signs of abating.

Acute febrile neutrophilic dermatosis, or Sweet’s Syndrome (SS), was first characterized by Dr. Robert Sweet in 1964 with eight cases of fever, neutrophilic polymorphonuclear leukocytosis, dermatological lesions, and histological evidence of dense dermal infiltration by mature neutrophils. SS presents in three settings idiopathic, malignancy-associated, and drug-induced. In 1996, Walker and Cohen outlined the current diagnostic criteriafordrug-induced SS withabrupt onset of painful lesions, dermal histology showing dense neutrophilic infiltrate, pyrexia>38°C, temporal relationship of drug administration to clinical presentation, and symptom resolutionfollowing drug withdrawal or systemic corticosteroid treatment. SS has rarely been reported in association with gynecologic malignancies.

Case Report.

A 41-year-old female receiving neoadjuvant chemotherapy for advanced high-grade serous ovarian carcinoma presented for evaluation of cyclic fevers with dermatologic lesions following treatment with Carboplmorbidity and long-term sequelae.

This case represents an example of SS in a patient receiving therapy with the most commonly implicated medication class, granulocyte colony-stimulating factor (GCSF). In drug-induced SS, there’s often a temporal relationship between medication administration and symptom development. In this case, all criteria for drug-induced SS were met with a GCS-F as the likely causative agent. This case illustrates a rare diagnosis in the context of gynecologic cancer treatment and will expand available reports of SS in the Gynecologic Oncology literature. We hope to elicit more prompt recognition and diagnosis of SS from practitioners to minimize patient morbidity and long-term sequelae.

Platinum-based chemotherapy and bevacizumab is the standard treatment for stage IVB cervical cancer. When metastases resolve, the benefit of radiating the primary tumor is unclear. We investigate the effect of pelvic radiation on PFS following chemotherapy and bevacizumab in stage IVB cervical cancer.

This is a retrospective series of 29 patients with stage IVB cervical cancer treated with platinum-based chemotherapy and bevacizumab. 3 subgroups were evaluated definitive pelvic radiation, palliative radiation, and no radiation. The primary outcome was the mean PFS. Progression was determined radiographically. Kaplan-Meier method and the log-rank test for equality analyzed OS and PFS.

The median OS was 38.4months. 11 patients (38%) received definitive radiation, 9 (31%) received palliative and 9 (31%) received no radiation. 7/8 in the palliative group, 7/10 who received no radiation and all in the definitive group experienced progression. The median PFS was 7.5months and not statistically different (p=0.oved oncologic outcomes. In absence of higher-level data, shared decision-making with consideration for comorbidities and performance status should be employed.The Adverse Outcome Pathway (AOP) concept is an emerging tool in regulatory toxicology that uses simplified descriptions to show cause-effect relationships between stressors and toxicity outcomes in intact organisms. The AOP structure is a modular framework, with Key Event Relationships (KERs) representing the unit of causal relationship based on existing knowledge, describing the connection between two Key Events. Because KERs are the only unit to support inference it has been argued recently that KERs should be recognized as the core building blocks of knowledge assembly within the AOP-Knowledge Base. Herein, we present a first case to support this proposal and provide a full description of a KER linking decreased all-trans retinoic acid (atRA) levels in developing ovaries with disrupted meiotic entry of oogonia. We outline the evidence to support a role for atRA in inducing meiosis in oogonia across mammals; this is important because elements of the RA synthesis/degradation pathway are recognized targets for numerous environmental chemicals. The KER we describe will be used to support an intended AOP linking inhibition of the atRA producing ALDH1A enzymes with reduced fertility in women.In this paper, we initially provide significant improvements on the computational aspects of dual Hahn moment invariants (DHMIs) in both 2D and 3D domains. These improvements ensure the numerical stability of DHMIs for large-size images. Then, we propose an efficient method for optimizing the local parameters of dual Hahn polynomials (DHPs) when computing DHMIs using the Sine-Cosine Algorithm (SCA). DHMIs optimized via SCA are used to propose new and robust zero-watermarking scheme applied to both 2D and 3D images. On one hand, the simulation results confirm the efficiency of the proposed computation of 2D and 3D DHMIs regarding the numerical stability and invariability. Indeed, the proposed computation method of 2D DHMIs allows to reach a relative error (RE) of the order ≈10-10 for images of size 1024 × 1024 with an execution time improvement ratio exceeds 70% ( ETIR  ≥ 70%), which validates the efficiently of the proposed computation method. On the other hand, the simulation and comparison outcomes clearly demonstrate the robustness of the proposed zero-watermarking scheme against various geometric attacks (translation, rotation, scaling and combined transformations), as well as against other common 2D and 3D image processing attacks (compression, filtering, noise addition, etc.).The development of novel PET imaging agents for synaptic vesicle glycoprotein 2A (SV2A) allowed for the in vivo detection of synaptic density changes, which are correlated with the progression and severity of a variety of neuropsychiatric diseases. While multiple ongoing clinical investigations using SV2A PET are expanding its applications rapidly, preclinical SV2A PET imaging in animal models is an integral component of the translation research and provides supporting and complementary information. Herein, we overview preclinical SV2A PET studies in animal models of neurodegenerative disorders and discuss the opportunities and practical challenges in small animal SV2A PET imaging. At the Yale PET Center, we have conducted SV2A PET imaging studies in animal models of multiple diseases and longitudinal SV2A PET allowed us to evaluate synaptic density dynamics in the brains of disease animal models and to assess pharmacological effects of novel interventions. In this article, we discuss key considerations when designing preclinical SV2A PET imaging studies and strategies for data analysis. Specifically, we compare the brain imaging characteristics of available SV2A tracers, i.e., [11C]UCB-J, [18F]SynVesT-1, [18F]SynVesT-2, and [18F]SDM-16, in rodent brains. We also discuss the limited spatial resolution of PET scanners for small brains and challenges of kinetic modeling. We then compare different injection routes and estimate the maximum throughput (i.e., number of animals) per radiotracer synthesis by taking into account the injectable volume for each injection method, injected mass, and radioactivity half-lives. In summary, this article provides a perspective for designing and analyzing SV2A PET imaging studies in small animals.

Migraine is a primary neurological disorder associated with complex brain activity. Recently, mounting evidence has suggested that migraine is underpinned by aberrant dynamic brain activity characterized by linear and non-linear changes across a variety of time scales. However, the abnormal dynamic brain activity at different time scales is still unknown in patients with migraine without aura (MWoA). This study aimed to assess the altered patterns of brain activity dynamics over different time scales and the potential pathophysiological mechanisms of alterations in patients with MWoA.

Multiscale entropy in 50 patients and 20 healthy controls (HCs) was calculated to investigate the patterns and altered brain complexity (BC) across five different time scales. Spearman rank correlation analysis between BC in regions showing significant intergroup differences and clinical scores (i.e., frequency of migraine attacks, duration, headache impact test) was conducted in patients with MWoA.

The spatial distributio could be linked to instability in pain transmission and modulation. Our findings provide new evidence for the hypothesis of abnormal dynamic brain activity in migraine.

Migraine is associated with alterations in dynamic brain activity in the sensorimotor network and DMN over multiple time scales. Time-varying BC within these regions could be linked to instability in pain transmission and modulation. Our findings provide new evidence for the hypothesis of abnormal dynamic brain activity in migraine.

The thyroid hormone has been demonstrated to be associated with nonalcoholic fatty liver disease (NAFLD) in different populations. However, the relationship between thyroid hormone and the degree of liver steatosis in overweight/obese subjects is still unclear. Liver ultra-sound attenuation (LiSA) is a newly developed ultrasound attenuation parameter for the analysis of hepatic steatosis. The study aimed to characterize the relationship between thyroid hormone and LiSA in overweight/obese participants.

This case-control study was performed in Ningbo First Hospital, China. A total of 24 lean, 66 overweight and 49 obese participants were consecutively recruited from January 2021 to May 2021. Thyroid hormone and other clinical features were measured. LiSA was acquired by using a Hepatus ultrasound machine. Multiple linear regression analyses were performed to examine associations of LiSA and clinic indices.

Obese subjects had higher LiSA, fT3 and TSH levels than lean participants of similar age and sex (

< 0.05). LiSA was positively associated with the fT3 level. The multiple linear regression analyses showed that fT3 (ß = 0.353,

< 0.001) was independently associated with LiSA in overweight/obese participants.

The fT3 level was independently associated with the degree of liver steatosis among the overweight/obese participants.

The fT3 level was independently associated with the degree of liver steatosis among the overweight/obese participants.During the COVID-19 pandemic, it is important to examine the variables that may affect the psychological distress and psychological well-being of individuals. This study aims to investigate the mediating effects of psychological resilience, fear of COVID-19, and psychological distress on the relationship between self-compassion and psychological well-being among Turkish adults. The participants of this study were chosen through the convenience sampling method. Participants consist of 617 Turkish adults, 461 (74.7%) females and 156 (25.3%) males. The participants’ ages vary between 18 and 24 (M age  = 30.44, SD = 11.45). The relations between variables were examined by bootstrapping procedure. The results showed that self-compassion, resilience, fear of COVID-19, psychological distress, and psychological well-being are significantly inter-correlated. Self-compassion significantly predicts psychological well-being through the mediating factors of resilience, fear of COVID-19, and psychological distress. It was also found that psychological distress is a mediating factor for the relationship between fear of COVID-19, resilience, and psychological well-being. The indirect effects of self-compassion on psychological well-being through mediating variables were found to be significant. Based on the findings, it can be said that self-compassion decreases individuals’ psychological distress and increases their well-being by increasing their resilience. Consequently, psychoeducational programs designed to increase self-compassion and resilience can be vital to support individuals’ mental health. In light of the literature, the results, implications, and limitations were discussed.

(Neo) adjuvant chemotherapy decreases the risk of recurrence and improves overall survival among breast cancer patients; however, delays in chemotherapy initiation are associated with adverse health outcomes. The causes of delay are complex and include interrelated social, economic, cultural, environmental, and health system factors. Project Start was a qualitative study designed to assess and identify the multilevel factors contributing to the barriers and facilitators of initiating chemotherapy.

Women diagnosed with primary invasive breast cancer who experienced ≥60 day delay in (neo) adjuvant chemotherapy initiation were included. Participants completed semi-structured interviews exploring barriers and facilitators to starting chemotherapy. Interviews were transcribed and coded to identify themes using the

analytic approach. This analysis included thorough examination of the data by advancing through iterative analytic phases to identify core topics within and across transcripts.

We enrolled (N=22to facilitate initiating chemotherapy. Multilevel interventions that engage the patient, family, community, and medical team may support the initiation of timely chemotherapy.

Activating women to be engaged in the treatment process across multiple levels appeared to facilitate initiating chemotherapy. Multilevel interventions that engage the patient, family, community, and medical team may support the initiation of timely chemotherapy.

To compare patients’ self-reported health-related quality of life (HRQoL) before and after total knee arthroplasty (TKA) and determine factors contributing to any heterogeneity in HRQoL.

This prospective multicenter observational study included 404 patients with knee osteoarthritis who underwent TKA between April 1, 2019 and December 30, 2019 and whose HRQoL was assessed preoperatively and 7 days and 1, 3, and 6 months postoperatively. Sociodemographic characteristics were assessed using a General Information Questionnaire, disability using the Knee Injury and Osteoarthritis Outcome Score (KOOS-PS), resting pain using the visual analogue scale (Pain-VAS), and HRQoL using the European Quality of Life Five Dimension Five Level (EQ-5D-5L) scale. The growth mixture model was used to identify group heterogeneity in the developmental trajectories of KOOS-PS, Pain-VAS, and EQ5D5L. Logistic regression was used to explore the factors influencing the developmental trajectories of factors affecting the developmentalerably after TKA. However, there was heterogeneity in the changes in HRQoL depending on patients’ socioeconomic status, exercise, and baseline knee function. Dynamic tracking of the HRQoL of TKA patients and timely provision of rehabilitation guidance will promote continuous improvement of the HRQoL of TKA patients.Single-stranded siRNA (ss-siRNA) refers to the antisense strand of siRNA, which plays the role of gene silencing. Since single-stranded RNA is unstable, the present study employed a homemade neutral cytidinyl/cationic lipid delivery system and chemical modifications to improve its stability. The results showed that with the aid of mixed lipids, ss-siRNA could knock down 40% of target mRNA at 25 nM. With 2′-F/2′-OMe, phosphorothioate and 5′-terminal phosphorylation, the optimized ss-siRNA could knock down 80% of target mRNA at 25 nM. Further knocking down AGO2, the ss-siRNAs could not effectively silence target mRNAs. Analysis of the biodistribution in vivo showed that ss-siRNA was less durable than siRNA, but spread more quickly to tissues. This study provides a safe and efficient ss-siRNA delivery and modification strategy, which lays the foundation for future works.Semiconductor quantum dots (QDs) are a promising luminescent phosphor for next-generation lightings and displays. In particular, QD-based white light-emitting diodes (WLEDs) are considered to be the candidate light sources with the most potential for application in displays. In this work, we synthesized quaternary/ternary core/shell alloyed CdZnSeS/ZnSeS QDs with high bright emission intensity. The QDs show good thermal stability by performing high temperature-dependent experiments that range from 295 to 433 K. Finally, the WLED based on the CdZnSeS/ZnSeS QDs exhibits a luminous efficiency (LE) of 28.14 lm/W, an external quantum efficiency (EQE) of 14.86%, and a warm bright sunlight close to the spectrum of daylight (Commission Internationale de l’éclairage (CIE) coordinates 0.305, 0.371). Moreover, the photoluminescence (PL) intensity, LE, EQE, and correlated color temperature (CCT) of as-prepared QD WLED remained relatively stable with only slight changes in the luminescence stability experiment.Youth homelessness is a growing crisis in the United States that is associated with a range of adverse outcomes. A variety of social service programs exist to address youth homelessness and its consequences, such as street outreach and diversion services, emergency shelters, transitional housing programs, and rapid rehousing services, among others. The coronavirus disease 2019 (COVID-19) pandemic reached the United States in early 2020, altering nearly every facet of daily life, including the way social service organizations structure and deliver their programming. To understand the implications of the pandemic on housing and homelessness services for youth, the current study examines data from interviews conducted with staff from a large non-profit in Austin, Texas, serving vulnerable transition-age youth. Through these interviews, programmatic changes that occurred as a result of COVID-19-as well as challenges and facilitators to service delivery-were identified. This article provides an overview of these key learnings, as well as recommendations derived from these key learnings, for other organizations adapting their housing and homelessness services in response to the COVID-19 pandemic.Few therapeutic specialty molecules from in vitro cultures beyond paclitaxel have come to market and although other more complex products like ginseng have also appeared, success has been limited. Often it is not the science that is limiting, but rather regulatory issues that limit considerations of potential products mainly because of costs in getting the product to market. Here we discuss broader thinking of such specialty molecules in the form of dietary supplements, nutraceuticals, herbal medicines, botanical drugs, and pure molecules along with potential complex products from a regulatory standpoint and especially within the realm of approved botanical drugs, e.g., Veregen and Fulyzaq, that have new drug applications (NDAs). The United States food and drug administration (US FDA) regulatory categories are used to provide examples of alternative product options that could prove useful for taking specialty molecules to market.

Compared with traditional surgery, laparoscopic surgery offers the advantages of smaller scars and rapid recovery and has gradually become popular. However, laparoscopic surgery has the limitation of low visibility and a lack of touch sense. As such, a physician may unexpectedly damage blood vessels, causing massive bleeding. In clinical settings, Doppler ultrasound is commonly used to detect vascular locations, but this approach is affected by the measuring angle and bone shadow and has poor ability to distinguish arteries from veins. To tackle these problems, a smart blood vessel detection system for laparoscopic surgery is proposed.

Based on the principle of near-infrared spectroscopy, the proposed instrument can access hemoglobin (HbT) parameters at several depths simultaneously and recognize human tissue type by using a neural network.

Using the differences in HbT and StO

between different tissues, vascular and avascular locations can be recognized. Moreover, a mechanically rotatable stick enables the physician to easily operate in body cavities. Phantom and animal experiments were performed to validate the system’s performance.

The proposed system has high ability to distinguish vascular from avascular locations at various depths.

The proposed system has high ability to distinguish vascular from avascular locations at various depths.The aim of this work was to synthesize new bis hydrazone derived from benzil in good yield, namely (1Z,2Z)-1,2-bis (3-Chlorophenyl Hydrazino) Benzil, encoded by 3-Cl BHB. The benzil (or 1,2-diphenyl ethanedione) reacts with 3-Cl phenyl hydrazine by reflux method using ethanol as solvent to obtain the target compound. The obtained product is depicted by UV-Vis, IR spectroscopy and XRD-crystals analysis. All various contacts intra and intermolecular found in 3-Cl BHB were determined by the X-ray diffraction technique performed on single crystals. On the other hand, the optimized geometric structure of 3-Cl BHB was computed by the DFT/B3LYP method with 6-31 G (d, p) level. So, the bond lengths and angles, frontier molecular orbitals (FMO), surface electrostatic potential of the molecule (MEP), global reactivity descriptors, Mulliken atomic charges, computed vibrational analysis and electronic absorption spectrum were determined to get a good understanding of the electronic properties and the active sites of 3-Cl BHB, then to compare them with experimental data. Additionally, a conformational study was carried out using the same method (DFT). The structure-activity relationships established through molecular docking studies showed that 3-Cl BHB structure strongly binds to the receptors Mpro (-8.90 Kcal/mol) and RdRp (-8.60 Kcal/mol) which confirm its inhibition activity against COVID-19.

Diabetes mellitus (DM) is associated with different clinical complications. The aim of this study was to explore the prevalence of RLS in people with diabetes mellitus and compare the risk of restless leg syndrome (RLS) between diabetic and non-diabetic population.

We searched for studies of RLS prevalence in DM through PubMed, Embase, and Web of Science. Two authors independently completed the literature screening, data extraction, and bias risk assessment of eligible studies. All observational studies that assessed the prevalence or risk of RLS in DM were included, where the diagnosis of RLS was based on the International Restless Legs Syndrome Study Group (IRLSSG). Percentages, odds ratio (OR) with 95% confidence intervals (CI) were used to assess pooled estimates of RLS prevalence and risk based on random-effects models. Newcastle-Ottawa-scale (NOS) or a modified NOS were used to evaluate the quality of studies.

A total of 42 studies, including 835,986 participants, met the eligibility criteria for oject for disciplines of excellence, West China Hospital, Sichuan University” (ZYJC18003).

This work was supported by the Basic Conditions Platform Construction Project of Sichuan Science and Technology Department (2019JDPT0015), and the „1・3・5 project for disciplines of excellence, West China Hospital, Sichuan University” (ZYJC18003).

In this review, we will discuss commonly encountered pediatric sleep disorders, their clinical presentations, evaluation, and management.

Sleep problems are common complaints in the pediatric population with an estimated prevalence of at least 25%. This review examines frequently seen pediatric sleep disorders including insomnia, obstructive sleep apnea, hypersomnolence, circadian rhythm sleep-wake disorders, parasomnias, and movement disorders. Their clinical manifestations vary, but left untreated, these sleep disorders result in significant impairment. A detailed sleep history is key component in the evaluation process. Other useful tools include sleep diaries, questionnaires, and actigraphy. Polysomnography is often required for diagnosis. Treatment varies depending on the underlying sleep disorder. Pharmacologic treatment is often limited due to the lack of studies of safety and efficacy in the pediatric population.

Sleep disorders are commonly encountered in the pediatric population. Their clinical manifestations vary, though without treatment, many result in significant impairment. Detailed sleep history is an essential part of the evaluation process, though polysomnography is often required. Treatment depends on the underlying diagnosis.

Sleep disorders are commonly encountered in the pediatric population. Their clinical manifestations vary, though without treatment, many result in significant impairment. Detailed sleep history is an essential part of the evaluation process, though polysomnography is often required. Treatment depends on the underlying diagnosis.Chronic stress is thought to be a major contributor to the onset of mental disorders such as anxiety disorders. Several studies have demonstrated a correlation between anxiety state and neuroinflammation, but the detailed mechanism is unclear. Chitinase-3-like 1 (CHI3L1) is expressed in several chronic inflammatorily damaged tissues and is well known to play a major role in mediating inflammatory responses. In the present study, we investigated the anxiolytic-like effect of N-Allyl-2-[(6-butyl-1,3-dimethyl-2,4-dioxo-1,2,3,4-tetrahydropyrido[2,3-d]pyrimidin-5-yl)sulfanyl]acetamide (G721-0282), an inhibitor of CHI3L1, on mice treated with chronic unpredictable mild stress (CUMS), as well as the mechanism of its action. We examined the anxiolytic-like effect of G721-0282 by conducting several behavioral tests with oral administration of G721-0282 to CUMS-treated BALB/c male mice. We found that administration of G721-0282 relieves CUMS-induced anxiety. Anxiolytic-like effects of G721-0282 have been shown to be associated with decreased expressions of CUMS-induced inflammatory proteins and cytokines in the hippocampus. The CUMS-elevated levels of CHI3L1 and IGFBP3 were inhibited by treatment with G721-0282 in vivo and in vitro. However, CHI3L1 deficiency abolished the anti-inflammatory effects of G721-0282 in microglial BV-2 cells. These results suggest that G721-0282 could lower CUMS-induced anxiety like behaviors by regulating IGFBP3-mediated neuroinflammation via inhibition of CHI3L1.

COVID-19 pandemic has shown that the multisystem involvement in COVID-infected patients is beyond the usual clinical manifestations of other respiratory viral illnesses. This study aims to evaluate the upshots of COVID-19 in women with preeclampsia.

This descriptive study was conducted in department of Obstetrics & Gynaecology at VMMC & Safdarjung Hospital (May-November 2020), wherein a retrospective review of the medical records of laboratory confirmed SARS CoV2-positive pregnant women (as per ICMR), with preeclampsia (as defined by ACOG guidelines), was done in the dedicated COVID labour ward. Primary outcome was incidence of preeclampsia in SARS CoV2 positive gravid females. Secondary outcomes were socio-demographic and maternal characteristics, severity of COVID-19 and foeto-maternal outcome.

During these 7 months, 38/302 (12.58%) SARS COV2-positive women presented with pre-eclampsia, either before or at the time of admission; amongst them 47.37% were primigravida. Severe preeclampsia was chronicled in 65.71% women. Around 20% women had severe COVID-19. All women with severe COVID19 required ICU stay, 5 requiring intubation. Three of these patients succumbed to their illness. Out of the 40 babies born to these women (including 2 twin pregnancies), 36.84% were premature deliveries. Seventeen (42.50%) babies had low birth weight. Although 82.50% were live births, five (12.50%) were intrauterine demise and 2 were early neonatal deaths.

Gravid women with preeclampsia infected with SARS CoV2 have comparative more severe illness, requiring more intensive care requirement and high maternal and neonatal morbidity.

Gravid women with preeclampsia infected with SARS CoV2 have comparative more severe illness, requiring more intensive care requirement and high maternal and neonatal morbidity.In a fuzzy multicriteria decision-making (MCDM) problem, a decision maker may have differing viewpoints on the relative priorities of criteria. However, traditional methods merge these viewpoints into a single one, which leads to an unrepresentative decision-making result. Several recent methods identify the multiple viewpoints of a decision maker by decomposing the decision maker’s fuzzy judgment matrix into several symmetric fuzzy subjudgment matrices, which is an inflexible strategy. To enhance flexibility, this study proposed a fuzzy geometric mean (FGM) decomposition-based fuzzy MCDM method in which FGM is applied to decompose a fuzzy judgment matrix into several fuzzy subjudgment matrices that can be asymmetric. These fuzzy subjudgment matrices are diverse and more consistent than the original fuzzy judgment matrix. The proposed methodology was applied to select the best choice from a group of smart technology applications for supporting mobile health care during and after the COVID-19 pandemic. According to the experimental results, the proposed methodology provided a novel approach to decomposing fuzzy judgment matrices and produced more diverse fuzzy subjudgment matrices.Biomedical researchers and biologists often search a large amount of literature to find the relationship between biological entities, such as drug-drug and compound-protein. With the proliferation of medical literature and the development of deep learning, the automatic extraction of biological entity interaction relationships from literature has shown great potential. The fundamental scope of this research is that the approach described in this research uses technologies like dynamic word vectors and multichannel convolution to learn a larger variety of relational expression semantics, allowing it to detect more entity connections. The extraction of biological entity relationships is the foundation for achieving intelligent medical care, which may increase the effectiveness of intelligent medical question answering and enhance the development of precision healthcare. In the past, deep learning methods have achieved specific results, but there are the following problems the model uses static word vectors, which cannot distinguish polysemy; the weight of words is not considered, and the extraction effect of long sentences is poor; the integration of various models can improve the sample imbalance problem, the model is more complex. The purpose of this work is to create a global approach for eliminating different physical entity links, such that the model can effectively extract the interpretation of the expression relationship without having to develop characteristics manually. To this end, a deep multichannel CNN model (MC-CNN) based on the residual structure is proposed, generating dynamic word vectors through BERT (Bidirectional Encoder Representation from Transformers) to improve the accuracy of lexical semantic representation and uses multihead attention to capture the dependencies of long sentences and by designing the Ranking loss function to replace the multimodel ensemble to reduce the impact of sample imbalance. Tested on multiple datasets, the results show that the proposed method has good performance.Structural variation (SV) is an important type of genome variation and confers susceptibility to human cancer diseases. Systematic analysis of SVs has become a crucial step for the exploration of mechanisms and precision diagnosis of cancers. The central point is how to accurately detect SV breakpoints by using next-generation sequencing (NGS) data. Due to the cooccurrence of multiple types of SVs in the human genome and the intrinsic complexity of SVs, the discrimination of SV breakpoint types is a challenging task. In this paper, we propose a convolutional neural network- (CNN-) based approach, called svBreak, for the detection and discrimination of common types of SV breakpoints. The principle of svBreak is that it extracts a set of SV-related features for each genome site from the sequencing reads aligned to the reference genome and establishes a data matrix where each row represents one site and each column represents one feature and then adopts a CNN model to analyze such data matrix for the prediction of SV breakpoints. The performance of the proposed approach is tested via simulation studies and application to a real sequencing sample. The experimental results demonstrate the merits of the proposed approach when compared with existing methods. Thus, svBreak can be expected to be a supplementary approach in the field of SV analysis in human tumor genomes.Endoscopic techniques in spine surgery are rapidly evolving, with operations becoming progressively safer and less invasive. Lumbar interbody fusion (LIF) procedures comprise many spine procedures that have benefited from endoscopic assistance and minimally invasive approaches. Though considerable variation exists within endoscopic LIF, similar principles and techniques are common to all types. Nonetheless, innovations continually emerge, requiring trainees and experienced surgeons to maintain familiarity with the domain and its possibilities. We present two illustrative cases of endoscopic transforaminal lumbar interbody fusion with a comprehensive literature review of the different approaches to endoscopic LIF procedures.

To study the effect of health education combined with personalized psychological nursing intervention on pregnancy outcome of pregnant women with gestational diabetes mellitus (GDM).

170 patients with GDM admitted to Guangdong Women and Children Hospital from January 2018 to December 2018 were selected as study subjects and randomly divided into two groups. During the period from diagnosis of GDM to termination of pregnancy, both groups were given routine education and routine examination, and the intervention group adopted health education combined with personalized psychological nursing interventions during pregnancy. The pregnancy weight, blood glucose index, compliance, disease awareness, self-adjustment management ability, satisfaction, and pregnancy outcome were measured before and after the intervention.

There were no statistically significant differences in pregnancy weight, fasting plasma glucose, and 2 h postprandial blood glucose between the two groups before intervention (

= 0.768, 0.605, better pregnancy outcome, which merits widespread promotion.Sepsis has high fatality rates. Early diagnosis could increase its curating rates. There were no reliable molecular biomarkers to distinguish between infected and uninfected patients currently, which limit the treatment of sepsis. To this end, we analyzed gene expression datasets from the GEO database to identify its mRNA signature. First, two gene expression datasets (GSE154918 and GSE131761) were downloaded to identify the differentially expressed genes (DEGs) using Limma package. Totally 384 common DEGs were found in three contrast groups. We found that as the condition worsens, more genes were under disorder condition. Then, random forest model was performed with expression matrix of all genes as feature and disease state as label. After which 279 genes were left. We further analyzed the functions of 279 important DEGs, and their potential biological roles mainly focused on neutrophil threshing, neutrophil activation involved in immune response, neutrophil-mediated immunity, RAGE receptor binding, long-chain fatty acid binding, specific granule, tertiary granule, and secretory granule lumen. Finally, the top nine mRNAs (MCEMP1, PSTPIP2, CD177, GCA, NDUFAF1, CLIC1, UFD1, SEPT9, and UBE2A) associated with sepsis were considered as signatures for distinguishing between sepsis and healthy controls. Based on 5-fold cross-validation and leave-one-out cross-validation, the nine mRNA signature showed very high AUC.Deep learning technology has recently played an important role in image, language processing, and feature extraction. In the past disease diagnosis, most medical staff fixed the images together for observation and then combined with their own work experience to judge. The diagnosis results are subjective, time-consuming, and inefficient. In order to improve the efficiency of diagnosis, this paper applies the deep learning algorithm to the online diagnosis and classification of CT images. Based on this, in this paper, the deep learning algorithm is applied to CT image online diagnosis and classification. Based on a brief analysis of the current situation of CT image classification, this paper proposes to use the Internet of things technology to collect CT image information and establishes the Internet of things to collect the CT image model. In view of image classification and diagnosis, the convolution neural network algorithm in the deep learning algorithm is proposed to diagnose and classify CT images, and several factors affecting the accuracy of classification are proposed, including the convolution number and network layer number. Using the CT image of the hospital brain for simulation analysis, the simulation results confirm the effectiveness of the deep learning algorithm. With the increase of convolution and network layer and the decrease of compensation, the accuracy of image classification will decline. Using the maximum pool method, reducing the step size can improve the classification effect. Using relu function as the activation function can improve the classification accuracy. In the process of large data set processing, appropriately adding a network layer can improve classification accuracy. In the diagnosis and analysis of brain CT images, the overall classification accuracy is close to 70%, and in the diagnosis of tumor diseases, the accuracy is higher, up to 80%.

Coronary heart disease (CHD) is considered an inflammatory relative disease. This study is aimed at analyzing the health information of serum interferon in CHD based on logistic regression and artificial neural network (ANN) model.

A total of 155 CHD patients diagnosed by coronary angiography in our department from January 2017 to March 2020 were included. All patients were randomly divided into a training set (

= 108) and a test set (

= 47). Logistic regression and ANN models were constructed using the training set data. The predictive factors of coronary artery stenosis were screened, and the predictive effect of the model was evaluated by using the test set data. All the health information of participants was collected. Expressions of serum IFN-

, MIG, and IP-10 were detected by double antibody sandwich ELISA. Spearman linear correlation analysis determined the relationship between the interferon and degree of stenosis. The logistic regression model was used to evaluate independent risk factors ofnd MIG are positively correlated with the degree of stenosis. The IP-10 and MIG are independent risk factors for coronary artery stenosis.

To explore the characteristics of magnetic resonance diffusion tensor imaging (DTI) parameters in patients with high cervical spinal myeloma and the evaluation of postoperative spinal cord function.

In recent years, 42 patients with high cervical spine myeloma were selected as the observation group, and 42 healthy volunteers were selected as the control group during the same period. The apparent dispersion coefficient (ADC), the fractional anisotropy (FA), the number of fiber bundles (FT), and the fiber bundle ratio (FTR) were compared between the two groups. The correlation between the ADC, FA, FT, FTR, and the International Standard for Neurological Classification of Spinal Cord Injury (ISNCSCI) score in the observation group were analyzed. Spinal cord function was evaluated using the Japanese Orthopaedic Association Score (JOA). Logistic regression model was used to analyze the factors affecting the recovery of spinal cord function after surgery. The receiver operating characteristic curve (ROC) was usmbination of ADC, FA, FT, FTR1, and FTR2 of the lesion layer predicted the AUC of spinal cord functional recovery was 0.941, which was better than the single prediction (

< 0.05).

The abnormal DTI parameter values of patients with high cervical spinal myeloma can better reflect the lack of spinal cord function, and they can effectively predict the recovery of the patient’s body function after surgery, providing a reference for clinical diagnosis and treatment.

The abnormal DTI parameter values of patients with high cervical spinal myeloma can better reflect the lack of spinal cord function, and they can effectively predict the recovery of the patient’s body function after surgery, providing a reference for clinical diagnosis and treatment.

This meta-analysis is aimed at systematically assessing the efficacy and prognosis of hemodialysis (HD) and peritoneal dialysis (PD) in the treatment of end-stage renal disease (ESRD).

China National Knowledge Infrastructure, VIP, SinoMed, Cochrane Library, PubMed, and Embase databases were searched for relevant studies to evaluate the two different dialysis methods for ESRD. The search time was set from 2010 to 2021. Meta-analysis was performed using Stata16.0. The treatment group received PD, while the control group was given HD.

Out of 317 articles initially retrieved, 14 studies were finally included in our meta-analysis. The analysis results showed that there was no marked difference in the 1-year survival rate between the two groups (RR = 1.05; 95% CI 1.00, 1.10;

> 0.05), but the incidence rate of adverse reactions in the treatment group was significantly lower than that in the control group (RR = 0.51; 95% CI 0.37, 0.70;

< 0.05). In addition, PD and HD treatments caused significant de of adverse reactions, improving the nutritional status, and therefore improving the quality of life of patients.