Music-based therapeutic interventions for medical school students with emotional regulation and mental health: a pre-post cohort study.

Purpose: Depression and anxiety are prevalent mental health challenges among college students. Music therapy has shown effectiveness in addressing depressive symptoms and enhancing psychosomatic functioning. This study aimed to evaluate the effectiveness of a 4-step structured music therapy program in improving mood and reducing symptoms of depression and anxiety among medical school students. Materials and methods: The self-controlled study involved 45 medical school students (21 men and 24 women) aged 18-24 years to examine the prevalence of depression and anxiety, common mental health issues among medical school students. Participants underwent psychological assessment using the Symptom Checklist-90 (SCL-90), Self-Rating Anxiety Scale (SAS), and Self-Rating Depression Scale (SDS). An 8-week music therapy intervention, comprising four steps-sociality, interaction, music lessons, and creative expression-was administered. Results: Before-intervention, 55.6% and 15.6% students were identified as suffering from depression and anxiety respectively. Post-intervention, significant reductions in psychological distress, particularly in the Global Severity Index (GSI) and Positive Symptom Total (PST) on the SCL-90 scale, were observed (P < 0.05). Male students exhibited notable improvements in various psychological symptoms compared to females. Junior grade students demonstrated greater improvements, and clinical medicine students exhibited significant enhancements in specific areas post-intervention. Conclusion: The structured music therapy program showed promising results in improving mood and regulating emotions among medical school students. Music therapy holds potential as a holistic approach to address mental health challenges in this demographic.

Role of Branched-Chain Amino Acids in Metabolic Changes of Polycystic Ovary Syndrome.

Importance: Polycystic ovary syndrome (PCOS) is a common endocrine syndrome with multiple causes and polymorphic clinical manifestations, which is one of the important causes of menstrual disorders in women of childbearing age. It has been found that branched-chain amino acids (BCAAs), a class of essential amino acids that cannot be synthesized by the human body, play a significant role in the metabolic changes of PCOS, which may be involved in the pathogenesis of PCOS. Objective: The purpose of this review is to summarize the relevance between BCAAs and metabolic abnormalities in PCOS and to explore their possible mechanisms. Evidence Acquisition: The evidence is mainly obtained by reviewing the literature on PubMed related to PCOS, BCAAs, and related metabolic abnormalities and conducting summary analysis. Results: The metabolism of BCAAs can affect the homeostasis of glucose metabolism, possibly by disrupting the balance of gut microbiota, activating mTORC1 targets, producing mitochondrial toxic metabolites, and increasing the expression of proinflammatory genes. The correlation between obesity and BCAAs in PCOS patients may be related to the gene expression of BCAA metabolism-related enzymes in adipose tissue. The association between BCAA metabolic changes and nonalcoholic fatty liver disease in PCOS patients has not been fully clarified, which may be related to the lipid accumulation caused by BCAAs. At present, it is believed that hyperandrogenism in patients with PCOS is not related to BCAAs. However, through the study of changes in BCAA metabolism in prostate cancer caused by hyperandrogenism, we speculate that the relationship between BCAAs and hyperandrogenism may be mediated by mTORC1 and amino acid transporters. Conclusions and Relevance: Review of prior articles reveals that BCAAs may be related to insulin resistance, obesity, nonalcoholic fatty liver, and hyperandrogenism in PCOS patients, and its mechanisms are complex, diverse, and interrelated. This review also discussed the mechanism of BCAAs and these metabolic disorders in non-PCOS patients, which may provide some help for future research.

Establishment of a Nucleic Acid Detection Method for Norovirus GII.2 Genotype Based on RT-RPA and CRISPR/Cas12a-LFS.

To establish a rapid detection method for norovirus GII.2 genotype, this study employed reverse transcription recombinase polymerase amplification (RT-RPA) combined with CRISPR/Cas12a and lateral flow strip (RT-RPA-Cas12a-LFS). Here, the genome of norovirus GII.2 genotype was compared to identify highly conserved sequences, facilitating the design of RT-RPA primers and crRNA specific to the conserved regions of norovirus GII.2. Subsequently, the reaction parameters of RT-RPA were optimized and evaluated using agar-gel electrophoresis and LFS. The results indicate that the conserved sequences of norovirus GII.2 were successfully amplified through RT-RPA at 37°C for 25 minutes. Additionally, CRISPR/Cas12a-mediated cleavage detection was achieved through LFS at 37°C within 10 minutes using the amplification products as templates. Including the isothermal amplification reaction time, the total time is 35 minutes. The established RT-RPA-Cas12a-LFS method demonstrated specific detection of norovirus GII.2, yielding negative results for other viral genomes, and exhibited an excellent detection limit of 10 copies/µl. The RT-RPA-Cas12a-LFS method was further compared with qRT-PCR by analyzing 60 food-contaminated samples. The positive conformity rate was 100%, the negative conformity rate was 95.45%, and the overall conformity rate reached 98.33%. This detection method for norovirus GII.2 genotype is cost-effective, highly sensitive, specific, and easy to operate, offering a promising technical solution for field-based detection of the norovirus GII.2 genotype.

A nomogram for predicting 28-day mortality in elderly patients with acute kidney injury receiving continuous renal replacement therapy: a secondary analysis based on a retrospective cohort study.

BACKGROUND: Acute kidney injury (AKI) is a common and serious condition, particularly among elderly patients. It is associated with high morbidity and mortality rates, further compounded by the need for continuous renal replacement therapy in severe cases. To improve clinical decision-making and patient management, there is a need for accurate prediction models that can identify patients at a high risk of mortality. METHODS: Data were extracted from the Dryad Digital Repository. Multivariate analysis was performed using least absolute shrinkage and selection operator (LASSO) logistic regression analysis to identify independent risk factors and construct a predictive nomogram for mortality within 28 days after continuous renal replacement therapy in elderly patients with acute kidney injury. The discrimination of the model was evaluated in the validation cohort using the area under the receiver operating characteristic curve (AUC), and calibration was evaluated using a calibration curve. The clinical utility of the model was assessed using decision curve analysis (DCA). RESULTS: A total of 606 participants were enrolled and randomly divided into two groups: a training cohort (n = 424) and a validation cohort (n = 182) in a 7:3 proportion. A risk prediction model was developed to identify independent predictors of 28-day mortality in elderly patients with AKI. The predictors included age, systolic blood pressure, creatinine, albumin, phosphorus, age-adjusted Charlson Comorbidity Index (CCI), Acute Physiology and Chronic Health Evaluation II (APACHE II) score, and sequential organ failure assessment (SOFA) score. These predictors were incorporated into a logistic model and presented in a user-friendly nomogram. In the validation cohort, the model demonstrated good predictive performance with an AUC of 0.799. The calibration curve showed that the model was well calibrated. Additionally, DCA revealed significant net benefits of the nomogram for clinical application. CONCLUSION: The development of a nomogram for predicting 28-day mortality in elderly patients with AKI receiving continuous renal replacement therapy has the potential to improve prognostic accuracy and assist in clinical decision-making.

The Effectiveness of Acupuncture for Low Back Pain: An Umbrella Review and Meta-Analysis.

Recommendations on the use of acupuncture in managing low back pain (LBP) vary across different guidelines. The methodological quality of existing systematic reviews and meta-analyses on this topic also demonstrates considerable diversity, potentially leading to biased conclusions. Therefore, we comprehensively searched PubMed, EMBASE, Web of Science, Cochrane Database of Systematic Reviews, and Chinese National Knowledge Infrastructure (CNKI) databases and conducted an umbrella review. Scrutiny was performed to ascertain whether primary studies within the systematic reviews and meta-analyses adhered to our inclusion criteria, followed by a meticulous reanalysis of pertinent data. Participant numbers, heterogeneity, publication bias, and excessive significance were taken into account when assessing the credibility of the evidence. For robustness, sensitivity analysis was performed using the leave-one-out method. The results of the umbrella review yielded highly suggestive evidence in favor of the immediate and short-term analgesic effects of acupuncture, with suggestive evidence supporting intermediate-term analgesic effects. However, the effectiveness of acupuncture on disability improvement has demonstrated weak to suggestive evidence. Evidence supporting the enhancement of quality of life by acupuncture is limited. The leave-one-out analysis corroborated the robustness of the meta-analysis, further confirming the credibility of the findings. This umbrella review indicated that the most significant advantage of acupuncture for LBP is its capacity to reduce pain.

Effects of meditation on neural responses to pain: A systematic review and meta-analysis of fMRI studies.

This systematic review investigates the impact of meditation on neural responses to pain, as measured by functional magnetic resonance imaging (fMRI). Up to March 2024, we conducted searches across four databases for human studies implementing fMRI to assess the efficacy of meditation for pain relief. Eighteen studies met the inclusion criteria. Our systematic review indicates that activation of the insula, anterior cingulate cortex, and orbitofrontal cortex is positively associated with meditation for pain relief, while activity in regions like the amygdala and medial prefrontal cortex is negatively correlated with pain relief. Meta-analyses consistently reveal the involvement of various brain regions, including the insula, putamen, amygdala, anterior cingulate cortex, precentral gyrus, postcentral gyrus, inferior parietal lobule, superior temporal gyrus, inferior frontal gyrus, and caudate nucleus, in meditation-induced alleviation of pain. These findings suggest ccthat meditation acts on specific brain regions related to pain, mood, and cognition, providing insight into the potential mechanisms underlying the pain-alleviating effects of meditation on both pain sensations and emotional experiences.

Generating High-Precision Force Fields for Molecular Dynamics Simulations to Study Chemical Reaction Mechanisms Using Molecular Configuration Transformer.

Theoretical studies on chemical reaction mechanisms have been crucial in organic chemistry. Traditionally, calculating the manually constructed molecular conformations of transition states for chemical reactions using quantum chemical calculations is the most commonly used method. However, this way is heavily dependent on individual experience and chemical intuition. In our previous study, we proposed a research paradigm that used enhanced sampling in molecular dynamics simulations to study chemical reactions. This approach can directly simulate the entire process of a chemical reaction. However, the computational speed limited the use of high-precision potential energy functions for simulations. To address this issue, we presented a scheme for training high-precision force fields for molecular modeling using a previously developed graph-neural-network-based molecular model, molecular configuration transformer. This potential energy function allowed for highly accurate simulations at a low computational cost, leading to more precise calculations of the mechanism of chemical reactions. We applied this approach to study a Claisen rearrangement reaction and a carbonyl insertion reaction catalyzed by manganese.

Chief complaints and computed tomography results in the emergency department: a three-year retrospective cohort study.

BACKGROUND: Computed tomography (CT) is frequently performed in the patients who admitted to the emergency department (ED), discharged but returned to ED within 72 h. It is unknown whether the main complaints of patients assist physicians to use CT effectively. This study aimed to find the association between chief complaints and the CT results. METHODS: This three-year retrospective cohort study was conducted in the ED of a tertiary medical center. Adult patients who returned to the ED after the index visit were included from 2019 to 2021. Demographics, pre-existing diseases, chief complaints, and CT region were recorded by independent ED physicians. A logistic regression model with an odds ratio (OR) and 95% confidence interval (CI) was used to determine the relationship between chief complaints and positive CT results. RESULTS: In total, 7,699 patients revisited ED after the index visit; 1,202 (15.6%) received CT. The top chief complaints in patients who received CT were abdominal pain, dizziness, and muscle weakness. Patients with abdominal pain or gastrointestinal symptoms had a significantly higher rate of positive abdominopelvic CT than those without it (OR 2.83, 95% CI 1.98-4.05, p < 0.001), while the central nervous system and cardiopulmonary chief complaints were not associated (or negatively associated) with new positive CT findings. CONCLUSION: Chief complaints of patients on revisit to the ED are associated with different yields of new findings when CT scans of the chest, abdomen and head are performed. Physicians should consider these differential likelihoods of new positive findings based on these data.

Voltage-gated potassium channel 1.3: A promising molecular target in multiple disease therapy.

Voltage-gated potassium channel 1.3 (Kv1.3) has emerged as a pivotal player in numerous biological processes and pathological conditions, sparking considerable interest as a potential therapeutic target across various diseases. In this review, we present a comprehensive examination of Kv1.3 channels, highlighting their fundamental characteristics and recent advancements in utilizing Kv1.3 inhibitors for treating autoimmune disorders, neuroinflammation, and cancers. Notably, Kv1.3 is prominently expressed in immune cells and implicated in immune responses and inflammation associated with autoimmune diseases and chronic inflammatory conditions. Moreover, its aberrant expression in certain tumors underscores its role in cancer progression. While preclinical studies have demonstrated the efficacy of Kv1.3 inhibitors, their clinical translation remains pending. Molecular imaging techniques offer promising avenues for tracking Kv1.3 inhibitors and assessing their therapeutic efficacy, thereby facilitating their development and clinical application. Challenges and future directions in Kv1.3 inhibitor research are also discussed, emphasizing the significant potential of targeting Kv1.3 as a promising therapeutic strategy across a spectrum of diseases.

Air Leakage Characteristics of Gas Boreholes in Deep Coal Seams and Application of Short-Hole Grouting and Plugging.

Drilling for gas extraction is widely used as the main approach to manage gas in mines. However, gas leakage during borehole extraction reduces the root cause of the effectiveness of gas extraction. Given that forming a normal hole in the prominent coal seam of the Qingdong Coal Mine is impossible and that air leakage leads to difficulties in prepumping gas in the coal seam, we selected coal seam 3# as the object of this study. First, qualitative analysis determined that the air leakage channel restricted effective gas extraction. Second, short-hole grouting and plugging were proposed to increase the concentration and efficiency of gas extraction from the coal wall, forming a barrier by blocking the fissure network in the plastic zone of the surrounding rock of the coal roadway and preventing the air inside the roadway from penetrating into the coal seam and gas extraction drill holes. Finally, evaluation of the gas extraction efficiency between the grouting test and comparison areas indicated that the initial gas extraction concentration of a single hole could reach 89% when the depth of the selected blocking hole was 15 m. Grouting slowly decreased the gas extraction concentration from 72 to 25%, which effectively improved the speed at which the gas content was reduced in the coal body. The study findings provide a field basis for similar mines to improve their gas extraction efficiency and extend their extraction time.

Prediction of high-risk emergency department revisits from a machine-learning algorithm: a proof-of-concept study.

BACKGROUND: High-risk emergency department (ED) revisit is considered an important quality indicator that may reflect an increase in complications and medical burden. However, because of its multidimensional and highly complex nature, this factor has not been comprehensively investigated. This study aimed to predict high-risk ED revisit with a machine-learning (ML) approach. METHODS: This 3-year retrospective cohort study assessed adult patients between January 2019 and December 2021 from National Taiwan University Hospital Hsin-Chu Branch with high-risk ED revisit, defined as hospital or intensive care unit admission after ED return within 72 hours. A total of 150 features were preliminarily screened, and 79 were used in the prediction model. Deep learning, random forest, extreme gradient boosting (XGBoost) and stacked ensemble algorithm were used. The stacked ensemble model combined multiple ML models and performed model stacking as a meta-level algorithm. Confusion matrix, accuracy, sensitivity, specificity and area under the receiver operating characteristic curve (AUROC) were used to evaluate performance. RESULTS: Analysis was performed for 6282 eligible adult patients: 5025 (80.0%) in the training set and 1257 (20.0%) in the testing set. High-risk ED revisit occurred for 971 (19.3%) of training set patients vs 252 (20.1%) in the testing set. Leading predictors of high-risk ED revisit were age, systolic blood pressure and heart rate. The stacked ensemble model showed more favourable prediction performance (AUROC 0.82) than the other models: deep learning (0.69), random forest (0.78) and XGBoost (0.79). Also, the stacked ensemble model achieved favourable accuracy and specificity. CONCLUSION: The stacked ensemble algorithm exhibited better prediction performance in which the predictions were generated from different ML algorithms to optimally maximise the final set of results. Patients with older age and abnormal systolic blood pressure and heart rate at the index ED visit were vulnerable to high-risk ED revisit. Further studies should be conducted to externally validate the model.

Generative Adversarial Networks Accurately Reconstruct Pan-Cancer Histology from Pathologic, Genomic, and Radiographic Latent Features.

Artificial intelligence models have been increasingly used in the analysis of tumor histology to perform tasks ranging from routine classification to identification of novel molecular features. These approaches distill cancer histologic images into high-level features which are used in predictions, but understanding the biologic meaning of such features remains challenging. We present and validate a custom generative adversarial network - HistoXGAN - capable of reconstructing representative histology using feature vectors produced by common feature extractors. We evaluate HistoXGAN across 29 cancer subtypes and demonstrate that reconstructed images retain information regarding tumor grade, histologic subtype, and gene expression patterns. We leverage HistoXGAN to illustrate the underlying histologic features for deep learning models for actionable mutations, identify model reliance on histologic batch effect in predictions, and demonstrate accurate reconstruction of tumor histology from radiographic imaging for a 'virtual biopsy'.

Biosynthetic pathway redesign in non-conventional yeast for enhanced production of cembratriene-ol.

Cembratriene-ol (CBT-ol), a plant-derived macrocyclic diterpene with notable insecticidal activity, has attracted considerable attention with respect to the development of sustainable and green biopesticides. Currently, CBT-ol production is limited by an inefficient and costly plant extraction strategy. Herein, CBT-ol production was enhanced by redesigning the CBT-ol biosynthetic pathway in Candida tropicalis, with subsequent truncation of CBT-ol synthase further increasing CBT-ol production. Moreover, bottlenecks in the CBT-ol biosynthetic pathway were eliminated by adjusting the gene dosage of the rate-limiting enzymes. Ultimately, the resulting strain C. tropicalis CPPt-03D produced 129.17 mg/L CBT-ol in shaking flasks (a 144-fold increase relative to that of the initial strain C01-CD) with CBT-ol production reaching 1,425.76 mg/L in a 5-L bioreactor, representing the highest CBT-ol titer reported to date. These findings provide a green process and promising platform for the industrial production of CBT-ol and lays the foundation for organic farming.

Optimizing the robustness of higher-low order coupled networks.

Enhancing the robustness of complex networks is of great practical significance as it ensures the stable operation of infrastructure systems. We measure its robustness by examining the size of the largest connected component of the network after initial attacks. However, traditional research on network robustness enhancement has mainly focused on low-order networks, with little attention given to higher-order networks, particularly higher-low order coupling networks(the largest connected component of the network must exist in both higher-order and low-order networks). To address this issue, this paper proposes robust optimization methods for higher-low order coupled networks based on the greedy algorithm and the simulated annealing algorithm. By comparison, we found that the simulated annealing algorithm performs better. The proposed method optimizes the topology of the low-order network and the higher-order network by randomly reconnecting the edges, thereby enhancing the robustness of the higher-order and low-order coupled network. The experiments were conducted on multiple real networks to evaluate the change in the robustness coefficient before and after network optimization. The results demonstrate that the proposed method can effectively improve the robustness of both low-order and higher-order networks, ultimately enhancing the robustness of higher-low order coupled networks.

Spatial Contraction Based on Velocity Variation for Natural Walking in Virtual Reality.

Virtual Reality (VR) offers an immersive 3D digital environment, but enabling natural walking sensations without the constraints of physical space remains a technological challenge. Previous VR locomotion methods, including game controller, teleportation, treadmills, walking-in-place, and redirected walking (RDW), have made strides towards overcoming this challenge. However, these methods also face limitations such as possible unnaturalness, additional hardware requirements, or motion sickness risks. This paper introduces "Spatial Contraction (SC)", an innovative VR locomotion method inspired by the phenomenon of Lorentz contraction in Special Relativity. Similar to the Lorentz contraction, our SC contracts the virtual space along the user's velocity direction in response to velocity variation. The virtual space contracts more when the user's speed is high, whereas minimal or no contraction happens at low speeds. We provide a virtual space transformation method for spatial contraction and optimize the user experience in smoothness and stability. Through SC, VR users can effectively traverse a longer virtual distance with a shorter physical walking. Different from locomotion gains, the spatial contraction effect is observable by the user and aligns with their intentions, so there is no inconsistency between the user's proprioception and visual perception. SC is a general locomotion method that has no special requirements for VR scenes. The experimental results of our live user studies in various virtual scenarios demonstrate that SC has a significant effect in reducing both the number of resets and the physical walking distance users need to cover. Furthermore, experiments have also demonstrated that SC has the potential for integration with existing locomotion techniques such as RDW.

Dracotangusions A and B, two new sesquiterpenes from Dracocephalum tanguticum Maxim. with anti-inflammatory activity.

Two new guaiane sesquiterpenoids were isolated from the dried aerial parts of Dracocephalum tanguticum Maxim., named as dracotangusions A (1) and B (2), together with four known sesquiterpenoids, which were identified as Curcumenone (3), (4Z,7Z,9Z)-11-Hydroxy-4,7,9-germacratriene-1,6-dione (4), Kobusone (5), and (1S,10S), (4S, 5S)-(+)-germacrone-1(10)-4-diepoxide (6). The structures of isolates were determined by UV, IR, HR-ESI-MS, and NMR analysis. What is noteworthy is that four known sesquiterpenoids were isolated for the first time from the genus of Dracocephalum L. All compounds inhibited the extremely significant difference (p < 0.01) in anti-inflammatory activity, suggesting that these compounds may be promising candidates as an anti-inflammatory agent.

The interaction of sex and age on outcomes in emergency medical services-treated out-of-hospital cardiac arrest: A 5-year multicenter retrospective analysis.

Background: Studies have established that sex and age influence outcomes following out-of-hospital cardiac arrest (OHCA). However, a knowledge gap exists regarding their interaction. This study aimed to investigate the interaction of age and sex and how they cooperatively influence OHCA outcomes. Methods: This retrospective cohort study included adult, nontraumatic OHCA patients admitted to a university hospital and its affiliated hospitals in Taiwan from January 2017 to December 2021. Data including sex, age, body mass index, cardiac rhythm, and resuscitation information in the emergency department (ED) were collected from medical records. The study outcomes encompassed survival to intensive care unit (ICU) admission, survival to hospital discharge, and a favorable neurological outcome. Multivariable logistic regression was performed to estimate the influence of sex on study outcomes. Results: We analyzed a total of 2,826 eligible subjects categorized into three groups: young (18-44 years, 149 males and 57 females), middle-aged (45-64 years, 524 males and 188 females), and old (≥65 years, 1,049 males and 859 females). Analysis of the effects of sex according to age stratification showed that old males had higher odds for survival to ICU admission (OR: 1.49, 95% CI: 1.21-1.83) and favorable neurological outcomes (OR: 2.74, 95% CI: 1.58-4.76) than did old females. Analysis of the effects of age according to sex stratification revealed that old males had lower odds for survival to hospital discharge (OR: 0.33, 95% CI: 0.21-0.51) and favorable neurological outcomes (OR: 0.26, 95% CI: 0.16-0.43) than did young males. Old females also showed the same trend as males, with lower odds for survival to hospital discharge (OR: 0.37, 95% CI: 0.17-0.78) and favorable neurological outcomes (OR: 0.11, 95% CI: 0.05-0.25) than did young females. Conclusions: The interaction between sex and age in patients with OHCA results in diverse outcomes. Within the same sex, age demonstrated varying effects on distinct outcomes.

MRE11 liberates cGAS from nucleosome sequestration during tumorigenesis.

Oncogene-induced replication stress generates endogenous DNA damage that activates cGAS-STING-mediated signalling and tumour suppression1-3. However, the precise mechanism of cGAS activation by endogenous DNA damage remains enigmatic, particularly given that high-affinity histone acidic patch (AP) binding constitutively inhibits cGAS by sterically hindering its activation by double-stranded DNA (dsDNA)4-10. Here we report that the DNA double-strand break sensor MRE11 suppresses mammary tumorigenesis through a pivotal role in regulating cGAS activation. We demonstrate that binding of the MRE11-RAD50-NBN complex to nucleosome fragments is necessary to displace cGAS from acidic-patch-mediated sequestration, which enables its mobilization and activation by dsDNA. MRE11 is therefore essential for cGAS activation in response to oncogenic stress, cytosolic dsDNA and ionizing radiation. Furthermore, MRE11-dependent cGAS activation promotes ZBP1-RIPK3-MLKL-mediated necroptosis, which is essential to suppress oncogenic proliferation and breast tumorigenesis. Notably, downregulation of ZBP1 in human triple-negative breast cancer is associated with increased genome instability, immune suppression and poor patient prognosis. These findings establish MRE11 as a crucial mediator that links DNA damage and cGAS activation, resulting in tumour suppression through ZBP1-dependent necroptosis.