To investigate the awareness of cognitive impairment disorders among residents of the Xizang Autonomous Region and its influencing factors, thereby providing a basis for targeted prevention and treatment efforts.

The somatosensory system, including modalities such as touch, temperature, and pain, is essential for perceiving and interacting with the environment. When individuals encounter different somatosensory modalities, they interact through a process called multimodal somatosensory integration. This integration is essential for accurate perception, motor coordination, pain management, and adaptive behavior. Disruptions in this process can lead to a variety of sensory disorders and complicate rehabilitation efforts. However, research on the behavioral patterns and neural mechanisms underlying multimodal somatosensory integration remains limited. According to previous studies, multimodal somatosensory integration can result in facilitative or inhibitory effects depending on factors like stimulus type, intensity, and spatial proximity. Facilitative effects are observed primarily when stimuli from the same sensory modality (e.g., two touch or temperature stimuli) are presented simultaneously, leading to amplified perceptual strength and quicker reaction times. Additionally, certain external factors, such as cooling, can increase sensitivity to other sensory inputs, further promoting facilitative integration. In contrast, inhibitory effects may also emerge when stimuli from different sensory modalities interact, particularly between touch and pain. Under such conditions, one sensory input (e.g., vibration or non-noxious temperature stimulation) can effectively reduce the perceived intensity of the other, often resulting in reduced pain perception. These facilitative and inhibitory interactions are critical for efficient processing in a multi-stimulus environment and play a role in modulating the experience of somatosensory inputs in both normal and clinical contexts. The neural mechanisms underlying multimodal somatosensory integration are multi-tiered, encompassing peripheral receptors, the spinal cord, and various cortical structures. Facilitative integration relies on the synchronous activation of peripheral receptors, which transmit enhanced signals to higher processing centers. At the cortical level, areas such as the primary and secondary somatosensory cortex, through multimodal neuron responses, facilitate combined representation and amplification of sensory signals. In particular, the thalamus is a significant relay station where multisensory neurons exhibit superadditive responses, contributing to facilitation by enhancing signal strength when multiple inputs are present. Inhibitory integration, on the other hand, is mediated by mechanisms within the spinal cord, such as gating processes that limit transmission of competing sensory signals, thus diminishing the perceived intensity of certain inputs. At the cortical level, lateral inhibition within the somatosensory cortex plays a key role in reducing competing signals from non-target stimuli, enabling prioritized processing of the most relevant sensory input. This layered neural architecture supports the dynamic modulation of sensory inputs, balancing facilitation and inhibition to optimize perception. Understanding the neural pathways involved in somatosensory integration has potential clinical implications for diagnosing sensory disorders and developing therapeutic strategies. Future research should focus on elucidating the specific neural circuitry and mechanisms that contribute to these complex interactions, providing insights into the broader implications of somatosensory integration on behavior and cognition. In summary, this review highlights the importance of multimodal somatosensory integration in enhancing sensory perception. It also underscores the need for further exploration into the neural underpinnings of these processes to advance our understanding of sensory integration and its applications in clinical settings.

Objective To analyse the clinical efficiency of endolymphatic sac surgery (ESS) in the management of Meniere’s disease (MD). Methods A retrospective analysis was conducted on 274 patients with MD who were hospitalized for treatment in Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University from January 2009 to August 2023. All patients received lifestyle management and drug treatment such as diuretics. For those whose conditions were not well controlled 3 to 6 months after the initial treatment, intratympanic glucocorticoid (ITG) or ESS treatment was carried out. Six months after the treatment, the classes of vertigo relief and hearing changes in the patients were evaluated. After adjusting the confounding factors through propensity score matching (PSM), the impact of ESS on the prognosis of MD patients was evaluated. Results Among 274 patients, 194 and 80 patients underwent ITG and ESS, respectively. Eighty patients were enrolled into each group after PSM. Before and after PSM, the rate of patients reaching vertigo relief class A in ESS group was higher than that in the ITG group (P＝0.004); there was no significant difference in hearing preservation between the two groups. Kaplan-Meier curve analysis showed that vertigo relief in the ESS group was better than that in the ITG group (P=0.029); there was no statistically significant difference in hearing preservation between the two groups. Conclusion When the initial treatment for patients with MD is ineffective, choosing ESS is more beneficial than ITG for controlling vertigo.

BACKGROUND:It has been shown that Gushukang affects bone metabolism by regulating nucleotide and amino acid metabolism and immune mechanisms.Current research on the mechanism of Gushukang in the treatment of osteoporosis primarily focuses on osteoblast regulation and requires further improvement from the perspective of osteoclasts. OBJECTIVE:To investigate the mechanism by which Gushukang interferes with osteoclasts in the treatment of osteoporosis using RAW264.7 cells as the research model. METHODS:Twenty-four 8-week-old female Sprague-Dawley rats were randomly divided into four groups(n=6 per group):the three experimental groups were given 1,2 and 4 g/kg osteoporosis solution by gavage(2 times per day),and the control group was given an equal amount of distilled water by gavage(2 times per day).After 7 days of intragastric administration,aortic blood samples were extracted to collect serum samples using centrifugation,and serum samples from the same groups were combined to obtain the low-,medium-,and high-concentration Gushukang-containing and normal sera for the subsequent experiments.(1)RAW264.7 cells were cultured in six groups:normal serum was added to the control group;low,medium,and high concentration groups were added with low,medium,and high concentrations of Gushukang-containing serum,respectively;ML385,a nuclear factor erythroid 2-related factor 2(Nrf2)inhibitor was given in the Nrf2 inhibitor group;and t-BHQ,a Nrf2 activator,was added in the Nrf2 activator group.Cell viability was detected using the cell counting kit-8 assay.(2)The 3rd generation RAW 264.7 cells were cultured and divided into five groups:the blank control group was added with normal serum,the osteoclast group was added with receptor activator of nuclear factor κB ligand(RANKL),and the low-,medium-,and high-concentration groups were added with low-,medium-,and high-concentration Gushukang-containing serum based on the addition of RANKL.Tartrate-resistant acid phosphate staining was performed after 5 days of culture.(3)RAW264.7 cells were cultured and divided into five groups:blank control group was cultured with normal serum,osteoclast group cultured with normal serum and RANKL,high concentration+osteoclast group cultured with RANKL+high concentration Gushukang-containing serum,osteoclast+Nrf2 agonist group cultured with RANKL+t-BHQ,and high concentration+osteoclast+Nrf2 inhibitor group cultured with RANKL+high concentration Gushukang-containing serum+ML385.Western blot assay and determination of reactive oxygen content were performed after 5 days of culture. RESULTS AND CONCLUSION:The cell counting kit-8 results indicated that Gushukang-containing serum,NRF2 inhibitor or agonist had no significant effect on RAW264.7 cell viability.Tartrate-resistant acid phosphate staining results demonstrated that Gushukang-containing serum exhibited a concentration-dependent inhibitory effect on osteoclast differentiation.Western blot analysis and determination of reactive oxygen species revealed that compared with the blank control group,Nrf2 protein expression was decreased in the osteoclast group(P<0.05),while c-Fos and NFATc1 protein expression and reactive oxygen species content were elevated(P<0.05);compared with the osteoclast group,Nrf2 protein expression was elevated and reactive oxygen species content was decreased in the high-concentration+osteoclast group,osteoclast+Nrf2 agonist group,and high-concentration+osteoclast+Nrf2 inhibitor group(P<0.05),while c-Fos and NFATc1 protein expression was decreased in the high concentration+osteoclast group and osteoclast+Nrf2 agonist group(P<0.05);compared with the high concentration+osteoclast group,Nrf2 protein expression was decreased(P<0.05)and reactive oxygen species content was elevated(P<0.05)in the high concentration+osteoclast+Nrf2 inhibitor group.To conclude,Gushukang reduces reactive oxygen species production by activating Nrf2,thereby inhibiting downstream of the c-Fos/NFATc1 pathway and suppressing osteoclast differentiation.

Obstructive sleep apnea (OSA) is an increasingly widespread sleep-breathing disordered disease, and is an independent risk factor for many high-risk chronic diseases such as hypertension, coronary heart disease, stroke, arrhythmias and diabetes, which is potentially fatal. The key to the prevention and treatment of OSA is early diagnosis and treatment, so the assessment and diagnostic technologies of OSA have become a research hotspot. This paper reviews the research progresses of severity assessment parameters and diagnostic technologies of OSA, and discusses their future development trends. In terms of severity assessment parameters of OSA, apnea hypopnea index (AHI), as the gold standard, together with the percentage of duration of apnea hypopnea (AH%), lowest oxygen saturation (LSpO₂), heart rate variability (HRV), oxygen desaturation index (ODI) and the emerging biomarkers, constitute a multi-dimensional evaluation system. Specifically, the AHI, which measures the frequency of sleep respiratory events per hour, does not fully reflect the patients’ overall sleep quality or the extent of their daytime functional impairments. To address this limitation, the AH%, which measures the proportion of the entire sleep cycle affected by apneas and hypopneas, deepens our understanding of the impact on sleep quality. The LSpO₂ plays a critical role in highlighting the potential severe hypoxic episodes during sleep, while the HRV offers a different perspective by analyzing the fluctuations in heart rate thereby revealing the activity of the autonomic nervous system. The ODI provides a direct and objective measure of patients’ nocturnal oxygenation stability by calculating the number of desaturation events per hour, and the biomarkers offers novel insights into the diagnosis and management of OSA, and fosters the development of more precise and tailored OSA therapeutic strategies. In terms of diagnostic techniques of OSA, the standardized questionnaire and Epworth sleepiness scale (ESS) is a simple and effective method for preliminary screening of OSA, and the polysomnography (PSG) which is based on recording multiple physiological signals stands for gold standard, but it has limitations of complex operations, high costs and inconvenience. As a convenient alternative, the home sleep apnea testing (HSAT) allows patients to monitor their sleep with simplified equipment in the comfort of their own homes, and the cardiopulmonary coupling (CPC) offers a minimal version that simply analyzes the electrocardiogram (ECG) signals. As an emerging diagnostic technology of OSA, machine learning (ML) and artificial intelligence (AI) adeptly pinpoint respiratory incidents and expose delicate physiological changes, thus casting new light on the diagnostic approach to OSA. In addition, imaging examination utilizes detailed visual representations of the airway’s structure and assists in recognizing structural abnormalities that may result in obstructed airways, while sound monitoring technology records and analyzes snoring and breathing sounds to detect the condition subtly, and thus further expands our medical diagnostic toolkit. As for the future development directions, it can be predicted that interdisciplinary integrated researches, the construction of personalized diagnosis and treatment models, and the popularization of high-tech in clinical applications will become the development trends in the field of OSA evaluation and diagnosis.

In recent years, the application of artificial intelligence (AI) in the field of biology has witnessed remarkable advancements. Among these, the most notable achievements have emerged in the domain of protein structure prediction and design, with AlphaFold and related innovations earning the 2024 Nobel Prize in Chemistry. These breakthroughs have transformed our ability to understand protein folding and molecular interactions, marking a pivotal milestone in computational biology. Looking ahead, it is foreseeable that the accurate prediction of various physicochemical properties of proteins—beyond static structure—will become the next critical frontier in this rapidly evolving field. One of the most important protein properties is thermodynamic stability, which refers to a protein’s ability to maintain its native conformation under physiological or stress conditions. Accurate prediction of protein stability, especially upon single-point mutations, plays a vital role in numerous scientific and industrial domains. These include understanding the molecular basis of disease, rational drug design, development of therapeutic proteins, design of more robust industrial enzymes, and engineering of biosensors. Consequently, the ability to reliably forecast the stability changes caused by mutations has broad and transformative implications across biomedical and biotechnological applications. Historically, protein stability was assessed via experimental methods such as differential scanning calorimetry (DSC) and circular dichroism (CD), which, while precise, are time-consuming and resource-intensive. This prompted the development of computational approaches, including empirical energy functions and physics-based simulations. However, these traditional models often fall short in capturing the complex, high-dimensional nature of protein conformational landscapes and mutational effects. Recent advances in machine learning (ML) have significantly improved predictive performance in this area. Early ML models used handcrafted features derived from sequence and structure, whereas modern deep learning models leverage massive datasets and learn representations directly from data. Deep neural networks (DNNs), graph neural networks (GNNs), and attention-based architectures such as transformers have shown particular promise. GNNs, in particular, excel at modeling spatial and topological relationships in molecular structures, making them well-suited for protein modeling tasks. Furthermore, attention mechanisms enable models to dynamically weigh the contribution of specific residues or regions, capturing long-range interactions and allosteric effects. Nevertheless, several key challenges remain. These include the imbalance and scarcity of high-quality experimental datasets, particularly for rare or functionally significant mutations, which can lead to biased or overfitted models. Additionally, the inherently dynamic nature of proteins—their conformational flexibility and context-dependent behavior—is difficult to encode in static structural representations. Current models often rely on a single structure or average conformation, which may overlook important aspects of stability modulation. Efforts are ongoing to incorporate multi-conformational ensembles, molecular dynamics simulations, and physics-informed learning frameworks into predictive models. This paper presents a comprehensive review of the evolution of protein thermodynamic stability prediction techniques, with emphasis on the recent progress enabled by machine learning. It highlights representative datasets, modeling strategies, evaluation benchmarks, and the integration of structural and biochemical features. The aim is to provide researchers with a structured and up-to-date reference, guiding the development of more robust, generalizable, and interpretable models for predicting protein stability changes upon mutation. As the field moves forward, the synergy between data-driven AI methods and domain-specific biological knowledge will be key to unlocking deeper understanding and broader applications of protein engineering.

Enterobacterales are important pathogens responsible for hospital-acquired infections and can cause various severe infectious diseases. Since their first report, extended-spectrum β-lactamase-producing Enterobacterales(ESBL-E) have become widely prevalent worldwide. These strains often exhibit multidrug resistance, significantly increasing the risk of treatment failure and patient mortality. In China, the prevention and control of ESBL-E face numerous challenges, including high prevalence rates, significant regional distribution disparities, uneven laboratory testing capabilities, and variations in antimicrobial stewardship and prescribing practices. To address these issues, Professors Zhuo Chao, Xu Yingchun and Yu Yunsong initiated the developmentof the Expert Consensus on Clinical Management Strategies for Infections Caused by Extended-Spectrum β-Lactamase-Producing Enterobacterales (2025), in collaboration with multidisciplinary experts. This consensus systematically reviews the global and domestic epidemiological characteristics of ESBL-E, clinical laboratory detection techniques, treatment principles and key therapeutic agents, as well as infection prevention and control measures. Furthermore, it elaborates on the microbiological features and stratified treatment strategies for different infection sources (including bloodstream infections, intracranial infections, respiratory and mediastinal infections, intra-abdominal infections, and urinary tract infections) and special populations (such as neutropenic patients with fever, emergency patients, and pediatric patients). The consensus aims to enhance the detection capacity of clinical laboratories for ESBL-E, standardize diagnosis and treatment strategies in healthcare settings, and provide scientific guidance to curb the spread of ESBL-E in China, thereby reducing associated morbidity and mortality.

Iron is indispensable for the viablility of nearly all living organisms, and it is imperative for cells, tissues, and organisms to acquire this essential metal sufficiently and maintain its metabolic stability for survival. Disruption of iron homeostasis can lead to the development of various diseases. There is a robust connection between iron metabolism and infection, immunity, inflammation, and aging, suggesting that disorders in iron metabolism may contribute to the pathogenesis of arthritis. Numerous studies have focused on the significant role of iron metabolism in the development of arthritis and its potential for targeted drug therapy. Targeting iron metabolism offers a promising approach for individualized treatment of arthritis. Therefore, this review aimed to investigate the mechanisms by which the body maintains iron metabolism and the impacts of iron and iron metabolism disorders on arthritis. Furthermore, this review aimed to identify potential therapeutic targets and active substances related to iron metabolism, which could provide promising research directions in this field.

Objective To compare early clinical and imaging results of domestic HURWA and imported Brainlab Knee3 surgical robot-assisted knee replacement.Methods A retrospective analysis was performed on 93 patients with knee os-teoarthritis(KOA)who underwent robot-assisted descending total knee arthroplasty(TKA)from January 2021 to July 2023,and they were divided into BRATKA group and HRATKA group according to use of robotic system.There were 40 patients in BRATKA group,including 16 males and 24 females,aged from 55 to 90 years old with an average of(64.3±7.0)years old;27 patients with grade Ⅲ and 13 patients with grade Ⅳ according to Kellgren-Lawrence(K-L);18 patients on the right side and 22 patients on the left side;the courses of disease ranged from 1 to 30 years with an average of(15.3±7.6)years;imported Brainlab Knee3 surgical robot assisted system was adopted.There were 53 patients in HRATKA group,including 18 males and 35 females,aged from 52 to 81 years old with an average of(64.4±8.5)years old;30 patients with grade Ⅲ and 23 patients with grade Ⅳ;21 patients on the right side and 32 patients on the left side;the courses of disease ranged from 1 to 32 years with an average of(16.4±7.9)years;HURWA surgical robot assisted system was adopted.Operation time,perioperative total blood loss,incision length and postoperative complications were compared between two groups.Deviation angle of hip-knee-an-kle angle(HKAA)before operation and on the first day after operation was compared between two groups.Later tibal compo-nent(LTC),frontal femoral component(FFC),later femoral component(LFC)and frontal tibal component(FTC)at 1 day af-ter on the first day after operation was compared between two groups.Knee Society score(KSS),visual analogue scale(VAS)and range of motion(ROM)of knee joint were compared between two groups before operation and on the 3rd and 90th day af-ter operation.Results Both groups were followed up for 11 to 18 months with an average of(14.4±2.1)months,and the wounds of all patients healed well.Operation time and incision length of BRATKA group were(132.1±34.6)min and(12.9±1.9)cm,while(94.1±10.8)min and(14.8±2.1)cm in HRATKA group,respectively,and the differences between two groups were statistically significant(P＜0.05).There were no significant difference in perioperative total blood loss and preoperative deviation angle of HKAA between two groups(P＞0.05).Deviation angle of HKAA,FFC angle and LFC angle in BRATKA group were(1.90±0.91)°,(87.90±1.51)°and(9.00±3.2)°,respectively;while(0.93±1.04)°,(89.03±0.96)° and(7.63±0.59)° in HRATKA group,respectively,and the differences between two groups were statistically significant(P＜0.05).There were no significant dif-ferences in FTC and LTC between two groups(P＞0.05).There were no significant differences in VAS of knee rest and exercise,KSS score and ROM of knee joint between two groups before operation and 3 days and 90 days after operation(P＞0.05).There was no significant difference in complications between two groups(P＞0.05).Conclusion Postoperative imaging of two robot systems showed good lower limb force line.The domestic HRATKA group had better LFC,FFC angle and HKA deviation angle than the imported BRATKA group,but there were no significant difference in postoperative knee function and pain relief.

Objective To monitor the susceptibility of clinical isolates to antimicrobial agents in tertiary hospitals in major regions of China in 2022.Methods Clinical isolates from 58 hospitals in China were tested for antimicrobial susceptibility using a unified protocol based on disc diffusion method or automated testing systems.Results were interpreted using the 2022 Clinical &Laboratory Standards Institute(CLSI)breakpoints.Results A total of 318 013 clinical isolates were collected from January 1,2022 to December 31,2022,of which 29.5％were gram-positive and 70.5％were gram-negative.The prevalence of methicillin-resistant strains in Staphylococcus aureus,Staphylococcus epidermidis and other coagulase-negative Staphylococcus species(excluding Staphylococcus pseudintermedius and Staphylococcus schleiferi)was 28.3％,76.7％and 77.9％,respectively.Overall,94.0％of MRSA strains were susceptible to trimethoprim-sulfamethoxazole and 90.8％of MRSE strains were susceptible to rifampicin.No vancomycin-resistant strains were found.Enterococcus faecalis showed significantly lower resistance rates to most antimicrobial agents tested than Enterococcus faecium.A few vancomycin-resistant strains were identified in both E.faecalis and E.faecium.The prevalence of penicillin-susceptible Streptococcus pneumoniae was 94.2％in the isolates from children and 95.7％in the isolates from adults.The resistance rate to carbapenems was lower than 13.1％in most Enterobacterales species except for Klebsiella,21.7％-23.1％of which were resistant to carbapenems.Most Enterobacterales isolates were highly susceptible to tigecycline,colistin and polymyxin B,with resistance rates ranging from 0.1％to 13.3％.The prevalence of meropenem-resistant strains decreased from 23.5％in 2019 to 18.0％in 2022 in Pseudomonas aeruginosa,and decreased from 79.0％in 2019 to 72.5％in 2022 in Acinetobacter baumannii.Conclusions The resistance of clinical isolates to the commonly used antimicrobial agents is still increasing in tertiary hospitals.However,the prevalence of important carbapenem-resistant organisms such as carbapenem-resistant K.pneumoniae,P.aeruginosa,and A.baumannii showed a downward trend in recent years.This finding suggests that the strategy of combining antimicrobial resistance surveillance with multidisciplinary concerted action works well in curbing the spread of resistant bacteria.