Risk factors analysis of lateral cervical lymph node metastasis in papillary thyroid carcinoma: a retrospective study of 830 patients.

OBJECTIVE: The aim of this study is to investigate the risk factors for lateral cervical lymph node metastasis in papillary thyroid carcinoma (PTC). METHODS: Clinicopathological data (age, gender, Hashimoto's thyroiditis, preoperative circulating tumor cells (CTCs), multifocal, maximum lesion diameter, invaded capsule, T stage, and lymph node metastasis) of 830 PTC patients diagnosed and treated in Meizhou People's Hospital from June 2021 to April 2023 were collected. The related factors of lateral cervical lymph node metastasis were analyzed. RESULTS: There were 334 (40.2%), and 103 (12.4%) PTC patients with central lymph node metastasis, and lateral cervical lymph node metastasis, respectively. Compared with patients without lateral cervical lymph node metastasis, PTC patients with lateral cervical lymph node metastasis had a higher proportion of multifocal, maximum lesion diameter > 1 cm, invaded capsule, T3-T4 stage. Regression logistic analysis showed that male (odds ratio (OR): 2.196, 95% confidence interval (CI): 1.279-3.769, p = 0.004), age < 55 years old (OR: 2.057, 95% CI: 1.062-3.988, p = 0.033), multifocal (OR: 2.759, 95% CI: 1.708-4.458, p < 0.001), maximum lesion diameter > 1 cm (OR: 5.408, 95% CI: 3.233-9.046, p < 0.001), T3-T4 stage (OR: 2.396, 95% CI: 1.241-4.626, p = 0.009), and invaded capsule (OR: 2.051, 95% CI: 1.208-3.480, p = 0.008) were associated with lateral cervical lymph node metastasis. CONCLUSIONS: Male, age < 55 years old, multifocal, maximum lesion diameter > 1 cm, T3-T4 stage, and invaded capsule were independent risk factors for lateral cervical lymph node metastasis in PTC.

FG-4592 protected haematopoietic system from ionising radiation in mice.

Ionising radiation exposure can lead to acute haematopoietic radiation syndrome. Despite significant advancements in the field of radioprotection, no drugs with high efficacy and low toxicity have yet been approved by the Food and Drug Administration. FG-4592, as a proline hydroxylase inhibitor, may play an important role in radioprotection of the haematopoietic system. Mice were peritoneal injected with FG-4592 or normal saline. After irradiation, the survival time, body weight, peripheral blood cell and bone marrow cell (BMC) count, cell apoptosis, pathology were analysed and RNA-sequence technique (RNA-Seq) was conducted to explore the mechanism of FG-4592 in the haematopoietic system. Our results indicated that FG-4592 improved the survival rate and weight of irradiated mice and protected the spleen, thymus and bone marrow from IR-induced injury. The number of BMCs was increased and protected against IR-induced apoptosis. FG-4592 also promoted the recovery of the blood system and erythroid differentiation. The results of RNA-Seq and Western blot showed that the NF-κB signalling pathway and hypoxia-inducible factor-1 (HIF-1) signalling pathway were upregulated by FG-4592. Meanwhile, RT-PCR results showed that FG-4592 could promote inflammatory response significantly. FG-4592 exhibited radioprotective effects in the haematopoietic system by promoting inflammatory response and targeting the NF-κB, HIF signalling pathway.

Diagnostic value of circulating tumor cells in patients with thyroid cancer: a retrospective study of 1478 patients.

BACKGROUND: Circulating tumor cell (CTC) detection is one form of liquid biopsy. It is a novel technique that is beginning to be applied in the field of thyroid cancer. The present study was designed to evaluate the diagnostic value of CTCs in patients with thyroid cancer. METHODS: A total of 1478 patients were retrospectively analyzed and divided into malignant group (n = 747) and benign group (n = 731). Peripheral blood was collected, and CTCs were enriched and quantified before surgery. The baseline data of the two groups were matched by Propensity Score Matching (PSM). Receiver operating characteristic (ROC) curves were used to evaluate the diagnostic efficiency of different indicators for thyroid cancer. The malignant group before PSM was further divided into subgroups according to the BRAF V600E mutation and lymphatic metastasis (N stage), and the number of CTCs in different subgroups was compared. RESULTS: After 1:1 PSM, baseline characteristics of the malignant group and benign group were matched and assigned 315 cases in each group. The number of CTCs and the TPOAb values were comparable in the two groups (p > 0.05). The TgAb values [1.890 (1.110 - 16.010) vs 1.645 (1.030 - 7.073) IU/mL, p = 0.049] were significantly higher in the malignant group than in the benign group. After PSM, ROC analyses showed that the areas under the curve (AUCs) of CTC, TgAb and ultrasound were 0.537 (sensitivity 65.6%, specificity 45.8%), 0.546 (sensitivity 40.0%, specificity 70.8%) and 0.705 (sensitivity 77.1%, specificity 63.2%), respectively. The AUCs of the combined detection of 'CTC + ultrasound' (combine 1) and the combined detection of 'CTC + TgAb + ultrasound' (combine 2) were 0.718 (sensitivity 79.3%, specificity 61.7%) and 0.724 (sensitivity 78.0%, specificity 63.3%), respectively. The AUC of ultrasound was significantly higher than CTC (p < 0.001). There was no statistically significant difference in AUC between combination 1 and ultrasound, and between combination 2 and ultrasound (p > 0.05). The number of CTCs between the N0 and N1 subgroups, and between the BRAF mutant and BRAF wild subgroups was comparable (p > 0.05). CONCLUSIONS: As an emerging and noninvasive testing tool, the efficacy of CTCs in diagnosing thyroid cancer is limited.

Identification and immunological role of cuproptosis in osteoporosis.

Background: osteoporosis is a skeletal disorder disease features low bone mass and poor bone architecture, which predisposes to increased risk of fracture. Copper death is a newly recognized form of cell death caused by excess copper ions, which presumably involve in various disease. Accordingly, we intended to investigate the molecular clusters related to the cuproptosis in osteoporosis and to construct a predictive model. Methods: we investigated the expression patterns of cuproptosis regulators and immune signatures in osteoporosis based on the GSE56815 dataset. Through analysis of 40 osteoporosis samples, we investigated molecular clustering on the basis of cuproptosis--related genes, together with the associated immune cell infiltration. The WGCNA algorithm was applied to detect cluster-specific differentially expressed genes. Afterwards, the optimum machine model was selected by calculating the performance of the support vector machine model, random forest model, eXtreme Gradient Boosting and generalized linear model. Nomogram, decision curve analysis, calibration curves, and the GSE7158 dataset was utilizing to confirm the prediction efficiency. Results: Differences between osteoporotic and non-osteoporotic controls confirm poorly adjusted copper death-related genes and triggered immune responses. In osteoporosis, two clusters of molecules in connection with copper death proliferation were outlined. The assessed levels of immune infiltration showed prominent heterogeneity between the different clusters. Cluster 2 was characterized by a raised immune score accompanied with relatively high levels of immune infiltration. The functional analysis we performed showed a close relationship between the different immune responses and specific differentially expressed genes in cluster 2. The random forest machine model showed the optimum discriminatory performance due to relatively low residuals and root mean square errors. Finally, a random forest model based on 5 genes was built, showing acceptable performance in an external validation dataset (AUC = 0.750). Calibration curve, Nomogram, and decision curve analyses also evinced fidelity in predicting subtypes of osteoporosis. Conclusion: Our study identifies the role of cuproptosis in OP and essentially illustrates the underlying molecular mechanisms that lead to OP heterogeneity.

Network analysis of spread of SARS-CoV-2 within dialysis clinics: A multi-center network analysis.

BACKGROUND: In-center hemodialysis entails repeated interactions between patients and clinic staff, potentially facilitating the spread of COVID-19. We examined if in-center hemodialysis is associated with the spread of SARS-CoV-2 between patients. METHODS: Our retrospective analysis comprised all patients receiving hemodialysis in four New York City clinics between March 12th, 2020, and August 31st, 2022. Treatment-level clinic ID, dialysis shift, dialysis machine station, and date of COVID-19 diagnosis by RT-PCR were documented. To estimate the donor-to-potential recipient exposure ("donor" being the COVID-19 positive patient denoted as "COV-Pos"; "potential recipient" being other susceptible patients in the same shift), we obtained the spatial coordinates of each dialysis station, calculated the Euclidean distances between stations and weighted the exposure by proximity between them. For each donor, we estimated the donor-to-potential recipient exposure of all potential recipients dialyzed in the same shift and accumulated the exposure over time within the 'COV-Pos infectious period' as cumulative exposures. The 'COV-Pos infectious period' started 5 days before COVID-19 diagnosis date. We deployed network analysis to assess these interactions and summarized the donor-to-potential recipient exposure in 193 network diagrams. We fitted mixed effects logistic regression models to test whether more donor-to-potential recipient exposure conferred a higher risk of SARS-CoV-2 infection. RESULTS: Out of 978 patients, 193 (19.7%) tested positive for COVID-19 and had contact with other patients during the COV-Pos infectious period. Network diagrams showed no evidence that more exposed patients would have had a higher chance of infection. This finding was corroborated by logistic mixed effect regression (donor-to-potential recipient exposure OR: 0.63; 95% CI 0.32 to 1.17, p = 0.163). Separate analyses according to vaccination led to materially identical results. CONCLUSIONS: Transmission of SARS-CoV-2 between in-center hemodialysis patients is unlikely. This finding supports the effectiveness of non-pharmaceutical interventions, such as universal masking and other procedures to control spread of COVID-19.

Scleroglucan protects the intestine from irradiation-induced injury by targeting the IL-17 signaling pathway.

BACKGROUND: Intestinal tissue is extremely sensitive to ionizing radiation (IR), which is easy to cause intestinal radiation sickness, and the mortality rate is very high after exposure. Recent studies have found that intestinal immune cells and intestinal stem cells (ISCs) may play a key role in IR-induced intestinal injury. METHODS: C57BL6 mice matched for age, sex and weight were randomly grouped and intraperitoneal injected with PBS, Scleroglucan (125.0 mg/kg) or Anti-mouse IL-17A -InVivo (10 mg/kg), the number of mice in each group was n ≥ 3.Survival time, body weight, pathology, organoids and immune cell markers of the mice after IR (10.0 Gy) were compared, and the mechanism of action in intestinal tissues was verified by transcriptome sequencing. RESULTS: Scleroglucan has significant radiation protective effects on the intestine, including improving the survival rate of irradiated mice, inhibiting the radiation damage of intestinal tissue, and promoting the proliferation and differentiation of intestinal stem cells (ISCs). The results of RNA sequencing suggested that Scleroglucan could significantly activate the immune system and up-regulate the IL-17 and NF-κB signaling pathways. Flow cytometry showed that Scleroglucan could significantly up-regulate the number of Th17 cells and the level of IL-17A in the gut. IL-17A provides radiation protection. After intraperitoneal injection of Scleroglucan and Anti-mouse IL-17A -InVivo, mice can significantly reverse the radiation protection effect of Scleroglucan, down-regulate the molecular markers of intestinal stem cells and the associated markers of DC, Th1 and Th17 cells, and up-regulate the associated markers of Treg and Macrophage cells. CONCLUSION: Scleroglucan may promote the proliferation and regeneration of ISCs by regulating the activation of intestinal immune function mediated by IL-17 signaling pathway and play a protective role in IR-induced injury.

Bowen's disease of the vulva and perianal area in a SLE patient successfully treated with Photodynamic therapy: A case report.

Photodynamic therapy (PDT) has emerged as a non-invasive treatment modality for superficial skin cancers. It has the advantage of greater tolerance and providing better cosmetic outcomes than conventional treatment methods. Because of the rarity of extensive Bowen's disease located in the genital area, evidence of efficacy for therapies is mainly based on case reports and clinical experience. This report presents a case of a 32-year-old female with Bowen's disease of the vulva and perianal area with systemic lupus erythematosus successfully treated by 5-aminolaevulinic acid PDT. There was no evidence of recurrence after five-years of follow-up.

High-Throughput Small Molecule Drug Screening For Age-Related Sleep Disorders Using Drosophila melanogaster.

Sleep, an essential component of health and overall well-being, often presents challenges for older individuals who frequently experience sleep disorders characterized by shortened sleep duration and fragmented patterns. These sleep disruptions also correlate with an increased risk of various illnesses in the elderly, including diabetes, cardiovascular diseases, and psychological disorders. Unfortunately, existing drugs for sleep disorders are associated with significant side effects such as cognitive impairment and addiction. Consequently, the development of new, safer, and more effective sleep disorder medications is urgently needed. However, the high cost and lengthy experimental duration of current drug screening methods remain limiting factors. This protocol describes a cost-effective and high-throughput screening method that utilizes Drosophila melanogaster, a species with a highly conserved sleep regulation mechanism compared to mammals, making it an ideal model for studying sleep disorders in the elderly. By administering various small compounds to aged flies, we can assess their effects on sleep disorders. The sleep behaviors of these flies are recorded using an infrared monitoring device and analyzed with the open-source data package Sleep and Circadian Analysis MATLAB Program 2020 (SCAMP2020). This protocol offers a low-cost, reproducible, and efficient screening approach for sleep regulation. Fruit flies, due to their short life cycle, low husbandry cost, and ease of handling, serve as excellent subjects for this method. As an illustration, Reserpine, one of the tested drugs, demonstrated the ability to promote sleep duration in elderly flies, highlighting the effectiveness of this protocol.

Predicting SARS-CoV-2 infection among hemodialysis patients using multimodal data.

Background: The coronavirus disease 2019 (COVID-19) pandemic has created more devastation among dialysis patients than among the general population. Patient-level prediction models for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection are crucial for the early identification of patients to prevent and mitigate outbreaks within dialysis clinics. As the COVID-19 pandemic evolves, it is unclear whether or not previously built prediction models are still sufficiently effective. Methods: We developed a machine learning (XGBoost) model to predict during the incubation period a SARS-CoV-2 infection that is subsequently diagnosed after 3 or more days. We used data from multiple sources, including demographic, clinical, treatment, laboratory, and vaccination information from a national network of hemodialysis clinics, socioeconomic information from the Census Bureau, and county-level COVID-19 infection and mortality information from state and local health agencies. We created prediction models and evaluated their performances on a rolling basis to investigate the evolution of prediction power and risk factors. Result: From April 2020 to August 2020, our machine learning model achieved an area under the receiver operating characteristic curve (AUROC) of 0.75, an improvement of over 0.07 from a previously developed machine learning model published by Kidney360 in 2021. As the pandemic evolved, the prediction performance deteriorated and fluctuated more, with the lowest AUROC of 0.6 in December 2021 and January 2022. Over the whole study period, that is, from April 2020 to February 2022, fixing the false-positive rate at 20%, our model was able to detect 40% of the positive patients. We found that features derived from local infection information reported by the Centers for Disease Control and Prevention (CDC) were the most important predictors, and vaccination status was a useful predictor as well. Whether or not a patient lives in a nursing home was an effective predictor before vaccination, but became less predictive after vaccination. Conclusion: As found in our study, the dynamics of the prediction model are frequently changing as the pandemic evolves. County-level infection information and vaccination information are crucial for the success of early COVID-19 prediction models. Our results show that the proposed model can effectively identify SARS-CoV-2 infections during the incubation period. Prospective studies are warranted to explore the application of such prediction models in daily clinical practice.

Estimating Time-Varying Treatment Switching Effect Using Accelerated Failure Time Model with Application to Vascular Access for Hemodialysis.

Vascular access for hemodialysis is of paramount importance. Although studies have found that central venous catheter (CVC) is often associated with poor outcomes and switching to arteriovenous fistula (AVF) and arteriovenous grafts (AVG) is beneficial, it has not been fully elucidated how the effect of switching of access on outcomes changes over time and whether the effect depends on switching time. In this paper we propose to relate the observed survival time for patients without access change and the counterfactual time for patients with access change using an AFT model with time-varying effects. The flexibility of AFT model allows us to account for baseline effect and the prognostic effect from covariates at access change while estimating the effect of access change. The effect of access change overtime is modeled nonparametrically using a cubic spline function. Simulation studies show excellent performance. Our methods are applied to investigate the effect of vascular access change over time in dialysis patients. It is concluded that the benefit of switching from CVC to AVG depends on the time of switching, the sooner the better.

Evaluating Urban Building Damage of 2023 Kahramanmaras, Turkey Earthquake Sequence Using SAR Change Detection.

On February 6, 2023 (local time), two earthquakes (Mw7.8 and Mw7.7) struck central and southern Turkey, causing extensive damage to several cities and claiming a toll of 40,000 lives. In this study, we propose a method for seismic building damage assessment and analysis by combining SAR amplitude and phase coherence change detection. We determined building damage in five severely impacted urban areas and calculated the damage ratio by measuring the urban area and the damaged area. The largest damage ratio of 18.93% is observed in Nurdagi, and the smallest ratio of 7.59% is found in Islahiye. We verified the results by comparing them with high-resolution optical images and AI recognition results from the Microsoft team. We also used pixel offset tracking (POT) technology and D-InSAR technology to obtain surface deformation using Sentinel-1A images and analyzed the relationship between surface deformation and post-earthquake urban building damage. The results show that Nurdagi has the largest urban average surface deformation of 0.48 m and Antakya has the smallest deformation of 0.09 m. We found that buildings in the areas with steeper slopes or closer to earthquake faults have higher risk of collapse. We also discussed the influence of SAR image parameters on building change recognition. Image resolution and observation geometry have a great influence on the change detection results, and the resolution can be improved by various means to raise the recognition accuracy. Our research findings can guide earthquake disaster assessment and analysis and identify influential factors of earthquake damage.

Ultrafiltration Rate Levels in Hemodialysis Patients Associated with Weight-Specific Mortality Risks.

BACKGROUND: We hypothesized that the association of ultrafiltration rate with mortality in hemodialysis patients was differentially affected by weight and sex and sought to derive a sex- and weight-indexed ultrafiltration rate measure that captures the differential effects of these parameters on the association of ultrafiltration rate with mortality. METHODS: Data were analyzed from the US Fresenius Kidney Care (FKC) database for 1 year after patient entry into a FKC dialysis unit (baseline) and over 2 years of follow-up for patients receiving thrice-weekly in-center hemodialysis. To investigate the joint effect of baseline-year ultrafiltration rate and postdialysis weight on survival, we fit Cox proportional hazards models using bivariate tensor product spline functions and constructed contour plots of weight-specific mortality hazard ratios over the entire range of ultrafiltration rate values and postdialysis weights (W). RESULTS: In the studied 396,358 patients, the average ultrafiltration rate in ml/h was related to postdialysis weight (W) in kg: 3W+330. Ultrafiltration rates associated with 20% or 40% higher weight-specific mortality risk were 3W+500 and 3W+630 ml/h, respectively, and were 70 ml/h higher in men than in women. Nineteen percent or 7.5% of patients exceeded ultrafiltration rates associated with a 20% or 40% higher mortality risk, respectively. Low ultrafiltration rates were associated with subsequent weight loss. Ultrafiltration rates associated with a given mortality risk were lower in high-body weight older patients and higher in patients on dialysis for more than 3 years. CONCLUSIONS: Ultrafiltration rates associated with various levels of higher mortality risk depend on body weight, but not in a 1:1 ratio, and are different in men versus women, in high-body weight older patients, and in high-vintage patients.

Roxadustat protect mice from DSS-induced colitis in vivo by up-regulation of TLR4.

BACKGROUND: The incidence of inflammatory bowel disease (IBD) is growing in the population. At present, the etiology of inflammatory bowel disease remains unclear, and there is no effective and low-toxic therapeutic drug. The role of the PHD-HIF pathway in relieving DSS-induced colitis is gradually being explored. METHODS: Wild-type C57BL/6 mice were used as a model of DSS-induced colitis to explore the important role of Roxadustat in alleviating DSS-induced colitis. High-throughput RNA-Seq and qRT-PCR methods were used to screen and verify the key differential genes in the colon of mice between normal saline (NS) and Roxadustat groups. RESULTS: Roxadustat could alleviate DSS-induced colitis. Compared with the mice in the NS group, TLR4 were significantly up-regulated in the Roxadustat group. TLR4 KO mice were used to verify the role of TLR4 in the alleviation of DSS-induced colitis by Roxadustat. CONCLUSION: Roxadustat has a repairing effect on DSS-induced colitis, and may alleviate DSS-induced colitis by targeting the TLR4 pathway and promote intestinal stem cell proliferation.

Machine Learning Case Study: Patterns of Kidney Function Decline and Their Association With Clinical Outcomes Within 90 Days After the Initiation of Renal Dialysis.

A case study explores patterns of kidney function decline using unsupervised learning methods first and then associating patterns with clinical outcomes using supervised learning methods. Predicting short-term risk of hospitalization and death prior to renal dialysis initiation may help target high-risk patients for more aggressive management. This study combined clinical data from patients presenting for renal dialysis at Fresenius Medical Care with laboratory data from Quest Diagnostics to identify disease trajectory patterns associated with the 90-day risk of hospitalization and death after beginning renal dialysis. Patients were clustered into 4 groups with varying rates of estimated glomerular filtration rate (eGFR) decline during the 2-year period prior to dialysis. Overall rates of hospitalization and death were 24.9% (582/2341) and 4.6% (108/2341), respectively. Groups with the steepest declines had the highest rates of hospitalization and death within 90 days of dialysis initiation. The rate of eGFR decline is a valuable and readily available tool to stratify short-term (90 days) risk of hospitalization and death after the initiation of renal dialysis. More intense approaches are needed that apply models that identify high risks to potentially avert or reduce short-term hospitalization and death of patients with a severe and rapidly progressive chronic kidney disease.

Predicting mortality risk in dialysis: Assessment of risk factors using traditional and advanced modeling techniques within the Monitoring Dialysis Outcomes initiative.

INTRODUCTION: Several factors affect the survival of End Stage Kidney Disease (ESKD) patients on dialysis. Machine learning (ML) models may help tackle multivariable and complex, often non-linear predictors of adverse clinical events in ESKD patients. In this study, we used advanced ML method as well as a traditional statistical method to develop and compare the risk factors for mortality prediction model in hemodialysis (HD) patients. MATERIALS AND METHODS: We included data HD patients who had data across a baseline period of at least 1 year and 1 day in the internationally representative Monitoring Dialysis Outcomes (MONDO) Initiative dataset. Twenty-three input parameters considered in the model were chosen in an a priori manner. The prediction model used 1 year baseline data to predict death in the following 3 years. The dataset was randomly split into 80% training data and 20% testing data for model development. Two different modeling techniques were used to build the mortality prediction model. FINDINGS: A total of 95,142 patients were included in the analysis sample. The area under the receiver operating curve (AUROC) of the model on the test data with XGBoost ML model was 0.84 on the training data and 0.80 on the test data. AUROC of the logistic regression model was 0.73 on training data and 0.75 on test data. Four out of the top five predictors were common to both modeling strategies. DISCUSSION: In the internationally representative MONDO data for HD patients, we describe the development of a ML model and a traditional statistical model that was suitable for classification of a prevalent HD patient's 3-year risk of death. While both models had a reasonably high AUROC, the ML model was able to identify levels of hematocrit (HCT) as an important risk factor in mortality. If implemented in clinical practice, such proof-of-concept models could be used to provide pre-emptive care for HD patients.

The coumarin-pyrazole dye for detection of hydrogen sulfide in cells.

Fluorescent probes for H2S are often interfered by other thiols. In this work, a coumarin-pyrazole dye with 2,4-dinitrosulfonyl group was designed for the detection of H2S. The probe exhibits weak fluorescence in water due to the photo induced electron transfer (PET) by 2,4-dinitrosulfonyl. After the sulfonyl group is cleaved off by H2S, strong fluorescence appears. The probe can specifically detect H2S without being interfered by other biological thiols, and shows a wide applicable pH range, low detection and wide detection range. The excellent detection properties of the probe can also be used to detect endogenous and exogenous H2S in cells. In addition, the probes can be made into portable test paper for the detection of H2S in solutions and can detect H2S in different water samples.

Zymosan-A promotes the regeneration of intestinal stem cells by upregulating ASCL2.

Intestinal stem cells (ISCs) are responsible for intestinal tissue homeostasis and are important for the regeneration of the damaged intestinal epithelia. Through the establishment of ionizing radiation (IR) induced intestinal injury model, we found that a TLR2 agonist, Zymosan-A, promoted the regeneration of ISCs in vivo and in vitro. Zymosan-A improved the survival of abdominal irradiated mice (81.82% of mice in the treated group vs. 30% of mice in the PBS group), inhibited the radiation damage of intestinal tissue, increased the survival rate of intestinal crypts and the number of ISCs after lethal IR in vivo. Through organoid experiments, we found that Zymosan-A promoted the proliferation and differentiation of ISCs after IR. Remarkably, the results of RNA sequencing and Western Blot (WB) showed that Zymosan-A reduced IR-induced intestinal injury via TLR2 signaling pathway and Wnt signaling pathway and Zymosan-A had no radioprotection on TLR2 KO mice, suggesting that Zymosan-A may play a radioprotective role by targeting TLR2. Moreover, our results revealed that Zymosan-A increased ASCL2, a transcription factor of ISCs, playing a core role in the process of Zymosan-A against IR-induced intestinal injury and likely contributing to the survival of intestinal organoids post-radiation. In conclusion, we demonstrated that Zymosan-A promotes the regeneration of ISCs by upregulating ASCL2.

How Temperature Influences Sleep.

Sleep is a fundamental, evolutionarily conserved, plastic behavior that is regulated by circadian and homeostatic mechanisms as well as genetic factors and environmental factors, such as light, humidity, and temperature. Among environmental cues, temperature plays an important role in the regulation of sleep. This review presents an overview of thermoreception in animals and the neural circuits that link this process to sleep. Understanding the influence of temperature on sleep can provide insight into basic physiologic processes that are required for survival and guide strategies to manage sleep disorders.