This study investigated the effects of compression garments on mitigating the decline in muscle strength and power resulting from exercise-induced muscle fatigue. Searches were performed in PubMed, Web of Science, EBSCO, Cochrane, and Scopus databases. The three-level restricted maximum likelihood random effects model was used to synthesize the data. Twenty-seven studies met the inclusion criteria. Compression garments had significant restorative effects on muscle strength (Hedges’s g = −0.21, p < 0.01) and power (Hedges’s g = −0.23, p < 0.01) after exercise-induced muscle fatigue. Subgroup analysis revealed that compression garments were effective in mitigating the decline in muscle strength when the rest intervals were 1–48 h and over 72 h and in mitigating the decline in power when the resting interval was 1–24 h. In addition, compression garments significantly mitigated the decline in muscle strength, during rest intervals of 1–24 h for trained individuals and over 72 h for both trained and untrained individuals, after exercise-induced muscle fatigue. In conclusion, compression garments significantly mitigated the decline in muscle strength after exercise-induced muscle fatigue. Both trained and untrained individuals could benefit from compression garments, with the effectiveness of compression garments being more pronounced in trained individuals compared to untrained ones.

The COVID-19 (C-19) pandemic has highlighted the significance of understanding the long-term effects of this disease on the quality of life of those infected. Long COVID-19 (L-C19) presents as persistent symptoms that continue beyond the main illness period, usually lasting weeks to years. One of the lesser-known but significant aspects of L-C19 is its impact on neuropsychiatric manifestations, which can have a profound effect on an individual’s quality of life. Research shows that L-C19 creates neuropsychiatric issues such as mental fog, emotional problems, and brain disease symptoms, along with sleep changes, extreme fatigue, severe head pain, tremors with seizures, and pain in nerves. People with cognitive problems plus fatigue and mood disorders experience great difficulty handling everyday activities, personal hygiene, and social interactions. Neuropsychiatric symptoms make people withdraw from social activity and hurt relationships, thus causing feelings of loneliness. The unpredictable state of L-C19 generates heavy psychological pressure through emotional suffering, including depression and anxiety. Neuropsychiatric changes such as cognitive impairment, fatigue, and mood swings make it hard for people to work or study effectively, which decreases their output at school or work and lowers their job contentment. The purpose of this narrative review is to summarize the clinical data present in the literature regarding the neuropsychiatric manifestations of L-C19, to identify current methods of diagnosis and treatment that lead to correct management of the condition, and to highlight the impact of these manifestations on patients’ quality of life.

Background: Gestational diabetes mellitus (GDM), characterized by gestational hyperglycemia due to insufficient insulin response, poses significant risks to both maternal and offspring health. Fetal exposure to maternal hyperglycemia leads to short-term complications such as macrosomia and neonatal hypoglycemia and long-term risks including obesity, metabolic syndrome, cardiovascular dysfunction, and type 2 diabetes. The Developmental Origins of Health and Disease (DOHaD) theory explains how maternal hyperglycemia alters fetal programming, increasing susceptibility to metabolic disorders later in life. Objective: This review explores the intergenerational impact of GDM, linking maternal hyperglycemia to lifelong metabolic, cardiovascular, and neurodevelopmental risks via epigenetic and microbiome alterations. It integrates the most recent findings, contrasts diagnostic methods, and offers clinical strategies for early intervention and prevention. Methods: A comprehensive literature search was conducted in PubMed, Scopus, and ScienceDirect to identify relevant studies published between 1 January 2000 and 31 December 2024. The search included studies focusing on the metabolic and developmental consequences of GDM exposure in offspring, as well as potential mechanisms such as epigenetic alterations and gut microbiota dysbiosis. Studies examining preventive strategies and management approaches were also included. Key Findings: Maternal hyperglycemia leads to long-term metabolic changes in offspring, with epigenetic modifications and gut microbiota alterations playing key roles. GDM-exposed children face increased risks of obesity, glucose intolerance, and cardiovascular diseases. Early screening and monitoring are crucial for risk reduction. Practical Implications: Understanding the intergenerational effects of GDM has important clinical implications for prenatal and postnatal care. Early detection, lifestyle interventions, and targeted postnatal surveillance are essential for reducing long-term health risks in offspring. These findings emphasize the importance of comprehensive maternal healthcare strategies to improve long-term outcomes for both mothers and their children.

Chronic kidney disease (CKD) involves inflammation, oxidative stress, and fibrosis, leading to renal dysfunction. Dapagliflozin, an SGLT2 inhibitor, shows renoprotective effects beyond glucose control, but its precise molecular mechanisms remain unclear. This study utilizes network pharmacology and molecular docking to elucidate its multi-target effects in CKD. Dapagliflozin’s SMILES structure was analyzed for ADMET properties. Potential targets were identified via SwissTargetPrediction, GeneCards, and SEA, and common CKD-related targets were determined. A protein–protein interaction (PPI) network was constructed, and key pathways were identified using GO and KEGG enrichment analyses. Molecular docking was conducted to validate dapagliflozin’s binding affinities with hub proteins. A total of 208 common targets were identified, including EGFR, GSK3β, and IL-6. GO and KEGG analyses highlighted key pathways, such as PI3K-Akt, MAPK, and AGE-RAGE, involved in inflammation, oxidative stress, and metabolic regulation. Molecular docking confirmed strong binding affinities with EGFR (−8.42 kcal/mol), GSK3β (−7.70 kcal/mol), and IL-6 (−6.83 kcal/mol). Dapagliflozin exhibits multi-target therapeutic potential in CKD by modulating inflammation, oxidative stress, and metabolic pathways. This integrative approach enhances the understanding of its mechanisms, supporting future experimental validation and clinical application in CKD management.

Continental rift zones on the early Earth provided essential conditions for the emergence of the first cells. These conditions included an abundant supply of raw materials, cyclic fluctuations in pressure and temperature over millions of years, and transitions of gases between supercritical and subcritical phases. While evidence supports vesicle formation and the chemical evolution of peptides, the mechanism by which information was stored remains unresolved. This study proposes a model illustrating how interactions among organic molecules may have enabled the encoding of amino acid sequences in RNA. The model highlights the interplay between three key molecular components: a proto-tRNA, the vesicle membrane, and short peptides. The vesicle membrane acted as a reservoir for hydrophobic amino acids and facilitated their attachment to proto-tRNA. As a single strand, proto-tRNA also served as proto-mRNA, enabling it to be read by charged tRNAs. By replicating this information and arranging RNA strands, the first functional peptides such as pore-forming proteins may have formed, thus improving the long-term stability of the vesicles. This model further outlines how these vesicles may have evolved into the earliest cells, with enzymes and larger RNA molecules giving rise to tRNA and ribosomal structures. Shearing forces may have facilitated the first cellular divisions, representing a pre-LUCA stage.

Extremophiles—organisms adapted to survive and thrive under extreme conditions—have fascinated scientists for decades, providing valuable insights into life’s resilience and offering untapped potential for technological innovation [...]

Artificial intelligence (AI)-assisted prediction of adverse drug reactions (ADRs) has significant potential for improving drug safety and reducing financial costs. Early studies often relied on limited dimensions such as the molecular structure of drugs or interactions with biomolecules. In contrast, integrating these characteristics provides valuable insights into ADR predictions from multiple perspectives, enhancing the comprehensiveness and accuracy of the prediction models. In addition, previous studies have focused on whether a specific adverse drug reaction occurs with a particular drug, ignoring the fact that multiple adverse drug reactions may occur concurrently with a single drug. To address these, we developed a predictor that identifies ADRs early in drug discovery, using a deep learning model designed to fuse multiple drug characteristics. Our approach employed four modules to extract one- and two-dimensional sequence structure information of drug molecules, drug–protein interaction data, and drug similarity. A fusion model integrated these characteristics to predict the precise probability of ADRs. The receiver operating characteristic–area under curve (ROC-AUC), area under precision–recall curve (AUPR), and F1 scores on the benchmark dataset are 0.7002, 0.6619, and 0.6330, respectively. The AUPR is significantly improved compared to the conventional multi-label classifier (from 64.02% to 66.19%). In addition, we compared the results with the state-of-the-art methods on LIU’s dataset and the AUPR increased from 34.65% to 68.82%, which shows that our model outperforms them in terms of accuracy and robustness. Ablation experiments further validated the effectiveness of the individual modules. This model accurately predicted the probability of various ADR classes by integrating comprehensive information, thereby offering significant value in enhancing monitoring measures for new drug development and clinical use.

In order to explore the effects of different irrigation methods on the physiological characteristics of mulch-free cotton in southern Xinjiang, the following experiments were carried out: (1) Different irrigation amount test: 300, 375, 450, 525, and 600 mm (represented by W1, W2, W3, W4, and W5) and a control (450 mm for film-covered cotton, represented by WCK) were set. (2) Drip irrigation frequency test: drip irrigation 12, 10, 8, and 6 times during the growth period (expressed by P12, P10, P8, and P6). Soil water dynamics, root distribution dynamics, chlorophyll fluorescence, leaf area index (LAI), SPAD (chlorophyll density), stress enzyme activities, and MDA (malondialdehyde) content were observed. The results showed that the average maximum change range of soil water content in the cotton field without film mulching was ±17.7%, which was 1.35 times higher than that in the cotton field with film mulching. Compared with cotton with film mulching, the root distribution characteristics of mulch-free cotton in the surface soil (0–20 cm) and the periphery (30 cm from the main root) decreased by 33.55–74.48% and 14.07–102.18%, respectively, while the root distribution characteristics in the deep layer (40–60 cm) increased by 49.62–242.67%, its average leaf green fluorescence parameters decreased by 9.03–50.44%, the activities of protective enzymes (SOD: superoxide dismutase, POD: peroxidase) decreased by 3.36–3.58%, the SPAD value decreased by 5.55%, and the MDA content increased by 3.17%, indicating that mulch-free cotton reduced the physiological function of cotton leaves, and the yield decreased by 42.07%. In the mulch-free treatments, the average root growth indexes were W2 > W3 > W4 > W5 > W1 and P12 > P10 > P8 > P6, and there was little difference between W2 and W3 and P12 and P10. With the increase in irrigation water and irrigation frequency, the initial fluorescence (F0) of leaves in each period of mulch-free cotton showed a downward trend, and the maximum fluorescence (Fm), variable fluorescence (FV), maximum photochemical efficiency (FV/Fm), potential photochemical activity of PS II (FV/F0), electron transfer of PS II (Fm/F0), and photosynthetic performance index (PIABS) showed an upward trend. In all water treatments, W3 and P12 had the highest SPAD value, protective enzyme activity, and the lowest MDA content, which was significantly different from other treatments except W4 and P10. The yield order of different treatments was W3 > W4 > W5 > W2 > W1, and the difference between W3 and W4 was not significant, but significant with W2 and W1. The irrigation frequency test was P12 > P10 > P8 > P6, and there was no significant difference between P12 and P10. We find that in the mulch-free treatment, all indicators of W3, W2, P12, and P10 were relatively high. It can be concluded that no mulching has a certain impact on cotton root distribution and leaf physiological function. When the irrigation amount is 450–525 mm and irrigation times is 10–12, it is beneficial for promoting root growth and plays a role in leaf physiological function, and the water use efficiency (WUE) is high, which can provide reference for the scientific water management of mulch-free cotton in production practice.

Background: Ductal carcinoma in situ (DCIS) is a precursor of invasive breast cancer and its early diagnosis and treatment are essential to prevent progression and recurrences. Risk stratification guidelines, such as the Van Nuys Prognostic Index (VNPI) and those by the National Comprehensive Cancer Network (NCCN), help guide appropriate treatment. This study compares VNPI recommendations for DCIS patients treated at Hospital do Servidor Público Estadual de São Paulo (HSPE) with NCCN guidelines, focusing on treatment conducted and recurrence rates. Methods: This retrospective, cross-sectional study reviewed medical records of 145 patients treated for DCIS at HSPE between January 1996 and June 2022, with a mean follow-up of 60.3 months. Results: Based on VNPI, 38.8% were low risk, 53.2% intermediate risk, and 7.8% high risk. NCCN guidelines classified only 12.9% as low risk and 87.1% as high risk. Treatment included breast-conserving surgery (BCS) with radiotherapy (43.1%), BCS alone (38.8%), and mastectomy (18.1%). There were 18 recurrences (15.5%): 5.2% as DCIS and 10.3% as invasive cancer. Of these recurrences, 5.6% occurred in patients who, according to NCCN, would have received BCS with radiotherapy or mastectomy. Conclusion: By integrating the VNPI with NCCN treatment guidelines, the NCCN’s recommendations could potentially reduce local recurrence rates by 5.6%. However, further studies are necessary to evaluate the long-term impact of these guidelines on overall survival outcomes.

Due to the routine use of endovascular revascularization and improved medical therapy, the majority of acute coronary syndrome (ACS) cases now have an uncomplicated course. However, in spite of the currently accepted secondary prevention standards, the residual risk of remote major adverse cardiovascular events (MACEs) after ACS remains high. Ultrasound carotid/subclavian atherosclerotic plaque assessment may represent an alternative approach to estimate the MACE risk after ACS and to control the quality of secondary prevention. Aim: To find the most important clinical predictors of MACEs in contemporary patients with predominantly uncomplicated ACS treated according to the Guidelines, and to study the potential of the longitudinal assessment of quantitative and qualitative ultrasound carotid/subclavian atherosclerotic plaque characteristics for MACE prediction after ACS. Methods: Patients with ACS, obstructive coronary artery disease (CAD) confirmed by coronary angiography, and carotid/subclavian atherosclerotic plaque (AP) who underwent interventional treatment were prospectively enrolled. The exclusion criteria were as follows: death or significant bleeding at the time of index hospitalization; left ventricular ejection fraction (EF) <30%; and statin intolerance. The clinical variables potentially affecting cardiovascular prognosis after ACS as well as the quantitative and qualitative AP characteristics at baseline and 6 months after the index hospitalization were studied as potential MACE predictors. Results: A total of 411 primary patients with predominantly uncomplicated ACS were included; AP was detected in 343 of them (83%). The follow-up period duration was 450 [269; 634] days. MACEs occurred in 38 patients (11.8%): seven—cardiac death, twenty-five—unstable angina/acute myocardial infarction, and six—acute ischemic stroke. In multivariate regression analyses, the most important baseline predictors of MACEs were diabetes (HR 2.22, 95% CI 1.08–4.57); the decrease in EF by every 5% from 60% (HR 1.22, 95% CI 1.03–1.46); the Charlson comorbidity index (HR 1.24, 95% CI 1.05–1.48); the non-prescription of beta-blockers at discharge (HR 3.24, 95% CI 1.32–7.97); and a baseline standardized AP gray scale median (GSM) < 81 (HR 2.06, 95% CI 1.02–4.19). Among the predictors assessed at 6 months, after adjustment for other variables, only ≥ 3 uncorrected risk factors and standardized AP GSM < 81 (cut-off value) at 6 months were significant (HR 3.11, 95% CI 1.17–8.25 and HR 3.77, 95% CI 1.43–9.92, respectively) (for all HRs above, all p-values < 0.05; HR and 95% CI values varied minimally across regression models). The baseline quantitative carotid/subclavian AP characteristics and their 6-month longitudinal changes were not associated with MACEs. All predictors retained significance after the internal validation of the models, and models based on the baseline predictors also demonstrated good calibration; the latter were used to create MACE risk calculators. Conclusions: In typical contemporary patients with uncomplicated interventionally treated ACS, diabetes, decreased EF, Charlson comorbidity index, non-prescription of beta-blockers at discharge, and three or more uncontrolled risk factors after 6 months were the most important clinical predictors of MACEs. We also demonstrated that a lower value of AP GSM reflecting the plaque vulnerability, measured at baseline and after 6 months, was associated with an increased MACE risk; this effect was independent of clinical predictors and risk factor control. According to our knowledge, this is the first demonstration of the independent role of longitudinal carotid/subclavian AP GSM assessment in MACE prediction after ACS.