Objective To analyze the correlation between cardiac function and degree of coronary artery lesion in elderly patients with coronary heart disease. Methods A total of 110 elderly patients with coronary heart disease and 30 elderly people without coronary heart disease (non-coronary heart disease group) in the Third People's Hospital of Chengdu were selected for the study. Based on Gensini score, the patients with coronary heart disease were classified into mild, moderate and severe lesion groups. The echocardiographic indicators and myocardial injury indicators [cardiac troponin I (cTnI), creatine kinase (CK), creatine kinase isoenzyme (CK-MB)] were compared among the groups. The correlation between cardiac function indicators and degree of coronary artery lesion and the evaluation value of various indicators on severe coronary artery lesion were analyzed. Results The left ventricular ejection fraction (LVEF) was represented as severe lesion groupmoderate lesion group>mild lesion group> non-coronary heart disease group (P<0.05). LVEF showed a negative correlation with degree of coronary artery lesion (P<0.05), while LVEDD, LVESD, cTnI, CK and CK-MB all exhibited positive correlations with degree of coronary artery lesion (P<0.05). The combined detection of the above indicators demonstrated superior evaluation efficiency on severe coronary artery lesion compared to any single indicator (P<0.05). Conclusion There is a significant correlation between cardiac function and degree of coronary artery lesion in elderly patients with coronary heart disease. Comprehensive evaluation of these indicators is conducive to clinical disease risk stratification and individualized treatment.

Spine fracture and dislocation are common traumatic spinal conditions that often require surgical intervention due to compromised spinal stability. Surgical approaches include anterior, posterior, and combined anterior-posterior spinal procedures. According to the specific surgical requirements, patients may be placed in the prone position or repositioned between prone and supine positions during surgery. Intraoperative repositioning has become an essential step in patient positioning. However, during repositioning, patients with spinal fracture and dislocation are at increased risk for complications such as hemodynamic instability, nerve injury, and pressure injuries to the skin and soft tissue. Notably, due to the instability of the spinal cord, even minor manipulations can further exacerbate the damage, potentially leading to severe outcomes like paraplegia. Although the current clinical guidelines provide instructive recommendations for standard position, there remains no specific protocols for intraoperative repositioning in patients with spine fracture and dislocation. With a concern for the lack of clinical studies on positioning techniques, risk prevention, and operational norms for special patients, no applicable guidelines or standards are available. A consensus was required to provide clinical reference, meet the requirements of surgical treatment, and minimize the safety risks of patients caused by improper placement of positions. Professional Committee of Operating Room Nursing of Shaanxi Nursing Association organized experts in nursing management and operating room nursing from major hospitals across China to formulate Expert consensus on intraoperative repositioning for patients with spinal fracture and dislocation ( version 2025). The consensus provides 11 recommendations covering pre-repositioning preparation, intraoperative maneuvers, and post-repositioning observation, aiming to provide references for clinical standardization of the intraoperative repositioning process and protection of patients′ safety.

Transformer models have emerged as pivotal tools within the realm of drug discovery,distinguished by their unique architectural features and exceptional performance in managing intricate data landscapes.Leveraging the innate capabilities of transformer architectures to comprehend intricate hierarchical dependencies inherent in sequential data,these models showcase remarkable efficacy across various tasks,including new drug design and drug target identification.The adaptability of pre-trained trans-former-based models renders them indispensable assets for driving data-centric advancements in drug discovery,chemistry,and biology,furnishing a robust framework that expedites innovation and dis-covery within these domains.Beyond their technical prowess,the success of transformer-based models in drug discovery,chemistry,and biology extends to their interdisciplinary potential,seamlessly combining biological,physical,chemical,and pharmacological insights to bridge gaps across diverse disciplines.This integrative approach not only enhances the depth and breadth of research endeavors but also fosters synergistic collaborations and exchange of ideas among disparate fields.In our review,we elucidate the myriad applications of transformers in drug discovery,as well as chemistry and biology,spanning from protein design and protein engineering,to molecular dynamics(MD),drug target iden-tification,transformer-enabled drug virtual screening(VS),drug lead optimization,drug addiction,small data set challenges,chemical and biological image analysis,chemical language understanding,and single cell data.Finally,we conclude the survey by deliberating on promising trends in transformer models within the context of drug discovery and other sciences.

Objective:To evaluate the effectiveness of anti-G straining maneuver (AGSM) in pilots by surface electromyography (sEMG), and to explore the relationships between characteristics of sEMG and anti-G endurance in pilots.Methods:Thirty-eight male high-performance fighter pilots who completed the human centrifuge test at the Air Force Medical Center were selected. Among them, 25 completed the 8.0 G for 10 s anti-G endurance test and 13 completed the 9.0 G for 10 s anti-G endurance test. The sEMG tester was used to keep track of the changes of sEMG in rectus abdominis, rectus femoris, anterior tibial and gastrocnemius muscles while pilots were engaged in AGSM. The anti-G endurance was evaluated according to the changes of visual fields and consciousness. The pilots were divided into 3 groups: the good vision and consciousness group, peripheral visual field narrowing group and endurance endpoint group. The differences in the integral electromyogram (iEMG), mean power frequency (MPF) and muscle input rates between the 3 groups were investigated.Results:A total of 25 pilots completed the 8.0 G for 10 s anti-G endurance test. Among them, 8 (32.0%) were in the good vision and consciousness group, 13 (52.0%) in the peripheral visual field narrowing group and 4 (16.0%) reached the endurance endpoint. Among the 13 pilots who completed the 9.0 G for 10 s anti-G endurance test, 3 (23.1%) were in the good vision and consciousness group, 6 (46.1%) in the peripheral visual field narrowing group, and 4 (30.8%) in the endurance endpoint group. The results of sEMG showed that the iEMG values of the anterior tibialis muscle in pilots under the 9.0 G for 10 s load were significantly different across endurance groups ( H=7.54, P=0.023), and that the iEMG values of the tibialis anterior muscle in the good vision and consciousness group were higher than those in the endurance endpoint group ( P=0.036). The negative slopes of MPF for the rectus abdominis, rectus femoris, anterior tibialis, and gastrocnemius muscles were higher in the good vision and consciousness group than in the other 2 groups, but the differences were not statistically significant ( P>0.05). During the 8.0 G for 10 s anti-G endurance test, there were significant differences in lower limb muscle contribution rates between the 3 groups ( F=4.19, P=0.029). The endurance endpoint group exhibited a lower contribution rate than the good vision and consciousness group ( P=0.025). During the 9.0 G for 10 s anti-G endurance test, there were significant differences in tibialis anterior muscle contribution rates between the 3 groups ( F=4.16, P=0.049). The endurance endpoint group demonstrated a lower contribution rate than the good vision and consciousness group ( P=0.049). Conclusions:The full and balanced activation of abdominal muscles and lower limb muscles, especially the effective mobilization of calf muscles, plays a pivotal role in improving pilots′ AGSM efficiency in high G environments.

Objective:To find out whether the performance of a China-made high performance human centrifuge can satisfy the need of high G training by conducting physiological experiments.Methods:The dynamic physical performance of the anti-G equipment with the human centrifuge was tested before 5 subjects underwent the gradual-onset rate (GOR) run and rapid onset rate (ROR) run experiments. The G onset rate of GOR was 0.1 G/s. The relaxed G-tolerance under GOR (GOR tolerance 1) and the anti-G straining maneuver aided G-tolerance under GOR (GOR tolerance 2) were tested respectively. The G onset rate of ROR was 3 G/s, and the closed-loop mode and pre-programed mode were employed respectively. The closed-loop mode involved 5 G 10 s and 8 G 10 s, where the subjects were required to manipulate the joystick to ensure that the real-time curve of the load matched the target curve. In the pre-programmed mode, the subjects were exposed to 8 G 10 s passively, without any operation requirements. A subjective evaluation form was filled out by subjects after the experiments, in which the 14 indexes for evaluation were about the gondola facilities and environment, running processes, medical monitoring and overall assessment.Results:Both the oxygen mask and anti-G suit achieved full pressurization within 2.0 s. The GOR tolerance 1 was [4.0(3.8, 4.6)] G while the GOR tolerance 2 was (6.2±0.5) G, suggesting a statistically significant difference ( Z=-2.63, P=0.008). The HP anti-G straining maneuver effect was (2.0±0.6) G. All the 5 subjects finished the 5 G 10 s experiment in a closed-loop mode. Three of them attempted 8 G 10 s in the closed-loop mode (1 subject achieved only 7.6 G peak acceleration, and the other 2 achieved full 8 G 10 s exposure), while the remaining 2 completed the 8 G 10 s in the pre-programmed mode. In the closed-loop mode, it was found that the stick force was too strong, the guiding G and real time G curve were not easy to distinguish for some of the subjects because the curve colors were similar, and that the subjects could not see the G curves clearly in case of a grayout. Both seat comfort and the voice quality of communication got the highest subjective assessment score [5.0(4.0, 5.0)] while the sensation of tumble got the lowest score (2.8±0.8). The median or mean scores of other subjective evaluation indexes ranged from 3.0 to 4.6 points. The overall score of subjective assessment was [4.0(3.5, 4.0)] points. Conclusions:The China-made high performance human centrifuge can meet the requirements of 8.0 G high G training, which can be made more effective and comfortable if the strong stick force and feeling of tumble during stop running are overcome.

Deep learning method can be used to automatically analyze electrocardiogram (ECG) data and rapidly implement arrhythmia classification, which provides significant clinical value for the early screening of arrhythmias. How to select arrhythmia features effectively under limited abnormal sample supervision is an urgent issue to address. This paper proposed an arrhythmia classification algorithm based on an adaptive multi-feature fusion network. The algorithm extracted RR interval features from ECG signals, employed one-dimensional convolutional neural network (1D-CNN) to extract time-domain deep features, employed Mel frequency cepstral coefficients (MFCC) and two-dimensional convolutional neural network (2D-CNN) to extract frequency-domain deep features. The features were fused using adaptive weighting strategy for arrhythmia classification. The paper used the arrhythmia database jointly developed by the Massachusetts Institute of Technology and Beth Israel Hospital (MIT-BIH) and evaluated the algorithm under the inter-patient paradigm. Experimental results demonstrated that the proposed algorithm achieved an average precision of 75.2%, an average recall of 70.1% and an average F ₁-score of 71.3%, demonstrating high classification accuracy and being able to provide algorithmic support for arrhythmia classification in wearable devices.
Humans ; Arrhythmias, Cardiac/diagnosis* ; Algorithms ; Electrocardiography/methods* ; Neural Networks, Computer ; Signal Processing, Computer-Assisted ; Deep Learning ; Classification Algorithms

Transformer models have emerged as pivotal tools within the realm of drug discovery, distinguished by their unique architectural features and exceptional performance in managing intricate data landscapes. Leveraging the innate capabilities of transformer architectures to comprehend intricate hierarchical dependencies inherent in sequential data, these models showcase remarkable efficacy across various tasks, including new drug design and drug target identification. The adaptability of pre-trained transformer-based models renders them indispensable assets for driving data-centric advancements in drug discovery, chemistry, and biology, furnishing a robust framework that expedites innovation and discovery within these domains. Beyond their technical prowess, the success of transformer-based models in drug discovery, chemistry, and biology extends to their interdisciplinary potential, seamlessly combining biological, physical, chemical, and pharmacological insights to bridge gaps across diverse disciplines. This integrative approach not only enhances the depth and breadth of research endeavors but also fosters synergistic collaborations and exchange of ideas among disparate fields. In our review, we elucidate the myriad applications of transformers in drug discovery, as well as chemistry and biology, spanning from protein design and protein engineering, to molecular dynamics (MD), drug target identification, transformer-enabled drug virtual screening (VS), drug lead optimization, drug addiction, small data set challenges, chemical and biological image analysis, chemical language understanding, and single cell data. Finally, we conclude the survey by deliberating on promising trends in transformer models within the context of drug discovery and other sciences.

Objective To explore the correlation between psoas major index(PMI)in patients with diffuse large B-cell lymphoma(DLBCL)and prognosis after rituximab,cyclophosphamide,doxorubicin,vincristine,prednisone(R-CHOP)chemotherapy.Methods Totally 148 DLBCL patients who received R-CHOP chemotherapy were retrospectively enrolled.Based on axis CT image at L3 vertebral transverse process,bilateral psoas muscles were delineated,and PMI was calculated.Then the patients were divided into muscle reduction group(≤lower quartile,n=37)and control group(＞lower quartile,n=111)according to the lower quartile of PMI.Clinical and imaging data were compared between groups,and the correlations of PMI with the above indexes were analyzed.The impact factors of prognosis of DLBCL were observed.Results Significant differences of body mass index(BMI),Eastern Cooperative Oncology Group(ECOG)score,international prognostic index(IPI),the proportion of B symptoms,mortality rate,3-year survival rate,β2-microglobulin,hemoglobin,albumin and PMI were found between groups(all P＜0.05).PMI of DLBCL patient was negatively correlated with age,ECOG score,IPI,β2-microglobulin and lactate dehydrogenase(LDH)(r=—0.126,-0.225,—0.262,—0.232,—0.229,all P＜0.05),but positively correlated with BMI and hemoglobin(r=0.282,0.376,both P＜0.05).The age,ECOG score,IPI and PMI were all independent impact factors of overall survival in DLBCL patients(all P＜0.05).Conclusion PMI was an independent impact factor of prognosis of DLBCL after R-CHOP chemotherapy.

OBJECTIVE: To evaluate the efficacy and safety of Shexiang Tongxin Dropping Pill (STDP) in treating stable angina patients with phlegm-heat and blood-stasis syndrome by exercise duration and metabolic equivalents. METHODS: This multicenter, randomized, double-blind, placebo-controlled clinical trial enrolled stable angina patients with phlegm-heat and blood-stasis syndrome from 22 hospitals. They were randomized 1:1 to STDP (35 mg/pill, 6 pills per day) or placebo for 56 days. The primary outcome was the exercise duration and metabolic equivalents (METs) assessed by the standard Bruce exercise treadmill test after 56 days of treatment. The secondary outcomes included the total angina symptom score, Chinese medicine (CM) symptom scores, Seattle Angina Questionnaire (SAQ) scores, changes in ST-T on electrocardiogram and adverse events (AEs). RESULTS: This trial enrolled 309 patients, including 155 and 154 in the STDP and placebo groups, respectively. STDP significantly prolonged exercise duration with an increase of 51.0 s, compared to a decrease of 12.0 s with placebo (change rate: -11.1% vs. 3.2%, P<0.01). The increase in METs was significantly greater in the STDP group than in the placebo group (change: -0.4 vs. 0.0, change rate: -5.0% vs. 0.0%, P<0.01). The improvement of total angina symptom scores (25.0% vs. 0.0%), CM symptom scores (38.7% vs. 11.8%), reduction of nitroglycerin consumption (100.0% vs. 11.3%), and all domains of SAQ, were significantly greater with STDP than placebo (all P<0.01). The changes in Q-T intervals at 28 and 56 days from baseline were similar between the two groups (both P>0.05). Twenty-five participants (16.3%) with STDP and 16 (10.5%) with placebo experienced AEs (P=0.131), with no serious AEs observed. CONCLUSION STDP could improve exercise tolerance in patients with stable angina and phlegm-heat and blood stasis syndrome, with a favorable safety profile. (Registration No. ChiCTR-IPR-15006020).
Humans ; Double-Blind Method ; Drugs, Chinese Herbal/adverse effects* ; Male ; Female ; Middle Aged ; Angina, Stable/physiopathology* ; Aged ; Syndrome ; Treatment Outcome ; Placebos ; Tablets

Type 2 diabetes (T2D) is an independent risk factor for cognitive impairment. The dysregulation of hypoxia inducible factor (HIF) signaling in T2D patients results in impaired adaptive responses to hypoxia, thereby accelerating the progression of complications. However, limited knowledge is available regarding its precise function in diabetes-associated cognitive impairment (DACI). Here, elevated HIF-1α levels were observed in brain endothelial cells (ECs) of db/db mice. Functionally, brain ECs-specific knockdown of H if1 a significantly ameliorated T2D-induced memory loss and neuronal damage. Glycolysis in brain ECs was inhibited in this process, as indicated by RNA-seq, leading to decreased hippocampal lactate production through reduced LDHA expression. Notably, T2D patients showed increased cerebrospinal fluid lactate levels, which were strongly associated with their cognitive dysfunction. Intrahippocampal injection of lactate accelerated cognitive dysfunction and impaired adult hippocampal neurogenesis (AHN) in db/db mice. Conversely, reducing hippocampal lactate levels through the intrahippocampal injection of oxamate delayed the onset of memory deficits. Furthermore, asiatic acid was discovered to protect db/db mice from cognitive impairment by decreasing brain endothelial HIF-1α expression and subsequently reducing hippocampal lactate-induced AHN damage. Overall, this study elucidates the inhibiting role played by endothelial HIF-1α-driven lactate in AHN and highlights a potential tactic of targeting HIF-1α in brain ECs for treating cognitive impairment.