Delirium is a common cause and complication of hospitalization in the elderly and is associated with higher risk of future dementia and progression of existing dementia, of which 70% is Alzheimer's disease (AD). AD and delirium, which are known to be aggravated by one another, represent significant societal challenges, especially in light of the absence of effective treatments. The intricate biological mechanisms have led to numerous clinical trial setbacks and likely contribute to the limited efficacy of existing therapeutics. Artificial intelligence (AI) presents a promising avenue for overcoming these hurdles by deploying algorithms to uncover hidden patterns across diverse data types. This review explores the pivotal role of AI in revolutionizing drug discovery for AD and delirium from target identification to the development of small molecule and protein-based therapies. Recent advances in deep learning, particularly in accurate protein structure prediction, are facilitating novel approaches to drug design and expediting the discovery pipeline for biological and small molecule therapeutics. This review concludes with an appraisal of current achievements and limitations, and touches on prospects for the use of AI in advancing drug discovery in AD and delirium, emphasizing its transformative potential in addressing these two and possibly other neurodegenerative conditions.

OBJECTIVES: To investigate the changes in blood levels of calcium and bone metabolism biochemical markers in patients with primary osteoporosis receiving treatment with denosumab. METHODS: Seventy-three patients with primary osteoporosis treated in our Department between December, 2021 and December 2023 were enrolled. All the patients were treated with calcium supplements, vitamin D and calcitriol in addition to regular denosumab treatment every 6 months. Blood calcium, parathyroid hormone (PTH), osteocalcin (OC), type I procollagen amino-terminal propeptide (PINP), and type I collagen carboxy-terminal telopeptide β special sequence (β‑CTX) data before and at 3, 6, 9, and 12 months after the first treatment were collected from each patient. RESULTS: Three months after the first denosumab treatment, the bone turnover markers (BTMs) OC, PINP, and β-CTX were significantly decreased compared to their baseline levels by 39.5% (P<0.001), 56.2% (P<0.001), and 81.8% (P<0.001), respectively. At 6, 9, and 12 months of treatment, OC, PINP, and β-CTX remained significantly lower than their baseline levels (P<0.001). Blood calcium level was decreased (P<0.05) and PTH level increased (P<0.05) significantly in these patients at months of denosumab treatment, but their levels were comparable to the baseline levels at 6, 9, and 12 months of the treatment (P>0.05). CONCLUSIONS Denosumab can suppress BTMs and has a good therapeutic effect in patients with primary osteoporosis, but reduction of blood calcium and elevation of PTH levels can occur during the first 3 months in spite of calcium supplementation. Blood calcium and PTH levels can recover the baseline levels as the treatment extended, suggesting the importance of monitoring blood calcium and PTH levels during denosumab treatment.
Humans ; Denosumab/therapeutic use* ; Calcium/blood* ; Parathyroid Hormone/blood* ; Biomarkers/blood* ; Osteoporosis/blood* ; Osteocalcin/blood* ; Procollagen/blood* ; Female ; Collagen Type I/blood* ; Peptide Fragments/blood* ; Bone Density Conservation Agents/therapeutic use* ; Bone and Bones/metabolism* ; Male ; Middle Aged ; Vitamin D ; Peptides/blood* ; Aged

Cancer multidrug resistance (MDR) impairs the therapeutic efficacy of various chemotherapeutics. Novel approaches, particularly the development of MDR reversal agents, are critically needed to address this challenge. This study demonstrates that tenacissoside I (TI), a compound isolated from Marsdenia tenacissima (Roxb.) Wight et Arn, traditionally used in clinical practice as an ethnic medicine for cancer treatment, exhibits significant MDR reversal effects in ABCB1-mediated MDR cancer cells. TI reversed the resistance of SW620/AD300 and KBV200 cells to doxorubicin (DOX) and paclitaxel (PAC) by downregulating ABCB1 expression and reducing ABCB1 drug transport function. Mechanistically, protein arginine methyltransferase 1 (PRMT1), whose expression correlates with poor prognosis and shows positive association with both ABCB1 and EGFR expressions in tumor tissues, was differentially expressed in TI-treated SW620/AD300 cells. SW620/AD300 and KBV200 cells exhibited elevated levels of EGFR asymmetric dimethylarginine (aDMA) and enhanced PRMT1-EGFR interaction compared to their parental cells. Moreover, TI-induced PRMT1 downregulation impaired PRMT1-mediated aDMA of EGFR, PRMT1-EGFR interaction, and EGFR downstream signaling in SW620/AD300 and KBV200 cells. These effects were significantly reversed by PRMT1 overexpression. Additionally, TI demonstrated resistance reversal to PAC in xenograft models without detectable toxicities. This study establishes TI's MDR reversal effect in ABCB1-mediated MDR human cancer cells through inhibition of PRMT1-mediated aDMA of EGFR, suggesting TI's potential as an MDR modulator for improving chemotherapy outcomes.
Humans ; Protein-Arginine N-Methyltransferases/antagonists & inhibitors* ; Drug Resistance, Neoplasm/drug effects* ; ErbB Receptors/genetics* ; Animals ; Cell Line, Tumor ; Drug Resistance, Multiple/drug effects* ; Methylation/drug effects* ; Saponins/administration & dosage* ; Mice ; Mice, Nude ; Mice, Inbred BALB C ; ATP Binding Cassette Transporter, Subfamily B/genetics* ; Doxorubicin/pharmacology* ; Paclitaxel/pharmacology* ; Female ; Repressor Proteins

Objective : To develop and to validate a combined model integrating18F-FDG PET/CT radiomics with clinical features to distinguish between lymphoma and lymphoid inflammatory hyperplasia. Methods : A retrospective study was conducted on a cohort of 232 patients diagnosed with lymphoma or lymphoid inflammatory hyperplasia. Comparative analyses of clinical and traditional imaging indicators were performed to identify inter-group differences. The clinical features were delineated and extracted using medical software including 3D-Slicer and Lifex. Selection of the features was performed to construct a PET/CT-based radiomics Logistic model, with a combined model integrating PET/CT with clinical features then used to evaluate the discriminative efficacy of these models. Results: Analysis of inter-group differences indicated that age, CTmean, and metabolic tumor volume(MTV)were effective for differentiating between lymphoma and lymphoid inflammatory hyperplasia(P<0.05). The PET/CT-based radiomics Logistic model differentiated between lymphoma and lymphoid inflammatory hyperplasia, with an area under curve(AUC) of 0.924(95%CI: 0.884-0.960) and 0.863(95%CI: 0.774-0.939) in the training and testing cohorts, respectively. The integrated Logistic model that combined PET/CT-based radiomics with clinical features to distinguish between lymphoma and lymphoid inflammatory hyperplasia achieved an AUC of 0.933(95%CI: 0.889-0.969) in the training cohort and 0.884(95%CI: 0.792-0.964) in the testing cohort. Decision curve analysis(DCA) demonstrated that the integrated model provided the greatest clinical net benefit. Conclusion The hybrid model integrating18F-FDG PET/CT radiomics with clinical features shows robust diagnostic efficacy to distinguish between lymphoma and lymphoid inflammatory hyperplasia.

Objective:To investigate the therapeutic efficacy of autologous hematopoietic stem cell transplantation (auto-HSCT) for treatment of diffuse large B-cell lymphoma (DLBCL) and the factors affecting the prognosis.Methods:A retrospective case series study was conducted. The clinical data of 51 patients with DLBCL who underwent auto-HSCT in the First Affiliated Hospital of Xinjiang Medical University from March 2019 to January 2024 were retrospectively analyzed. Patients were divided into high-risk group (19 cases) and non-high-risk group (low-risk, low-moderate-risk and moderate-high-risk groups, 32 cases) based on different risk stratifications; patients were divided into the germinal center B-cell (GCB) group (29 cases) and non-GCB group (22 cases) based on different cellular origins; patients were divided into BEAM group (39 cases) and BeEAM group (12 cases) based on different conditioning regimens before auto-HSCT; patients were divided into auto-HSCT consolidation therapy group (41 cases) and auto-HSCT after relapsed/refractory group (10 cases) based on different transplantation timings. The Kaplan-Meier method was used for survival analysis and log-rank was used for subgroup comparison.Results:All 51 patients achieved the hematopoietic reconstitution with no transplantation-related death within 100 d. Before auto-HSCT, 39 cases achieved complete remission and 12 cases (23.5%) achieved partial remission. After auto-HSCT, all cases achieved complete remission. Follow-up was until May 31, 2024, and the median follow-up time [ M ( Q1, Q3)] of 51 DLBCL patients was 33 (8, 43) months. After 51 DLBCL patients receiving auto-HSCT, 7 patients relapsed and 6 cases died including 3 cases with relapse-related death and 3 cases with non relapse-related death. The 3-year progression-free survival (PFS) and overall survival (OS) rates were 78.5% (95% CI: 64.4%-92.6%) and 85.5% (95% CI: 73.2%-97.8%), respectively. The 3-year PFS rate was 94.7% (95% CI: 84.7%-104.7%) in the high-risk group, 82.2% (95% CI: 67.9%-96.5%) in the non-high-risk group, and the difference in the PFS was not statistically significant between the high-risk group and the non-high-risk group ( P = 0.158). The 3-year PFS rate was 80.1% (95% CI: 64.4%-95.8%) in the GCB group, 88.1% (95% CI: 72.3%-104.2%) in the non-GCB group, and the difference in PFS was not statistically significant between the 2 groups ( P = 0.803). The 3-year PFS rate was 84.9% (95% CI: 72.6%-97.2%) in BEAM group, 61.1% (95% CI: 25.0%-97.2%) in the BeEAM group, and the difference in PFS was not statistically significant between the 2 groups ( P = 0.106). The 3-year PFS rate was 85.4% (95% CI: 73.4%-97.4%) in the auto-HSCT consolidation therapy group, 64.3% (95% CI: 31.4%-96.4%) in the auto-HSCT after relapsed/refractory group, and the difference in PFS was not statistically significant between the 2 groups ( P = 0.171). Conclusions:auto-HSCT is an effective therapy method for DLBCL.

Objective:To improve the understanding of recurrent adult Langerhans cell histiocytosis complicated with diabetes insipidus.Methods:The clinical data of 1 patient with recurrent adult Langerhans cell histiocytosis complicated with diabetes insipidus admitted to the First Affiliated Hospital of Shihezi University in January 2024 was collected. Its disease characteristics, effectiveness and safety of treatment scheme were analyzed, and literatures were reviewed.Results:The 42-year-old female patient was diagnosed as Langerhans cell histiocytosis in June 2021. After treated with cytarabine, the symptoms improved and the patient achieved sustained remission. In January 2024, the patient was admitted to the hospital due to pain in the middle part of the front chest. The PET-CT results indicated disease progression, which was manifested by new bone destruction, enlarged lymph nodes, and increased nocturnal urination, with urine volume of 3-5 L within 24 h. Based on the clinical manifestations such as cranial bone lesions, periorbital soft tissue lesions, and enlarged lymph nodes at onset, multiple systems and multiple foci involvement was considered. The diagnosis of combined diabetes insipidus was confirmed through the water deprivation and pressure test. After MACOP-B regimen (doxorubicin liposome, cyclophosphamide, vincristine, bleomycin, prednisone), the patient's bone pain was completely relieved, and no serious complications occurred.Conclusions:Recurrent adult Langerhans cell histiocytosis complicated with diabetes insipidus is rare; MACOP-B regimen is safe and effective in treatment of the disease.

Objective Depression,a most common psychiatric disease,is defined in Traditional Chinese Medicine(TCM)as Yu Syndrome,i.e.,depression disorder,or Baihe Disease,i.e.,lily bulb disease,a category of emotional disorders treated with lily-based TCM preparations.In TCM,depression is managed through syndrome differentiation and treatment,which is characterized by high efficacy and safety.However,there is no unified standard for the classification of depression syndromes,which leads to a disconnection between the analysis of patients'medication patterns and their actual syndromes and hinders the study of medication patterns specific to particular syndromes.Therefore,this study is focused on investigating the medication patterns of different sub-types of depression patients based on an objective classification system of depression.Methods We searched for and retrieved clinical literature on TCM formulas for depression from relevant databases,including China National Knowledge Infrastructure(CNKI),Wanfang Data,VIP Database,Sinomed,Web of Science,and PubMed.Information on patient symptoms and medication was standardized.Then,the symptoms and the medication frequency of depression patients were statistically analyzed.We used the K-means clustering method combined with implicit structural analysis to objectively categorize depression patients into sub-types.In addition,the main symptoms and core TCM formulas of each sub-type of depression patients were identified.On the basis of objective classification system,we also statistically analyzed the characteristics of herbs used on depression patients,including the 4 basic properties,the 5 flavors,the attributes,the therapeutic efficacy,and the co-occurrence patterns,which may help reveal the medication patterns.Results A total of 3 537 publications and 4 434 prescriptions were included in the analysis.By using the K-means algorithm and latent structure analysis methods,patients with depression were categorized into 9 sub-types,with Cluster 6 accounting for the largest proportion.The most common symptoms among depression patients were insomnia and a depressed mood.Medication frequency analysis showed that Radix Bupleuri(Chai Hu),Radix Paeoniae Alba(Bai Shao),Poria(Fu Ling),Rhizoma Chuanxiong(Chuan Xiong),and Radix Curcumae(Yu Jin)were the most commonly used TCM herbs.For the depression sub-types of Clusters 1,2,and 6,blood-activating and stasis-dissolving herbs were used most often.The depression sub-types of Clusters 3,4,5,8,and 9 were mainly treated with qi-regulating herbs,while the depression sub-type of Cluster 7 was treated with qi-supplementing herbs.Depression patients were mostly treated with herbs that were cold or warm in nature and had sweet,bitter,and pungent flavors.Moreover,treatments for Cluster 1 and Cluster 6 mainly targeted the spleen meridian,while those for Cluster 2,Cluster 3,Cluster 4 and Cluster 5 mainly targeted the heart meridian.The treatments for the other sub-types mainly targeted the liver meridian.The core TCM formulas for the 9 depression sub-types included Zishui Qinggan Decoction,Danzhi Xiaoyao Powder,Huanglian Wendan Tang,Chaihu Guizhi Tang,Modified Xiaoyao Powder,Qinggan Jieyu Tang,Xiaoyao Powder,Xuefu Zhuyu Decoction,and Bazhen Decoction.The most commonly used Chinese herbal medicinal formulas were Gan Cao-Chai Hu,Bai Shao-Chai Hu,and Chen Pi-Chai Hu.Conclusion Based on machine learning,this study reveals the scientific aspects of TCM typing and syndrome-based treatment.It clarifies the rationale for targeting different symptoms in depression treatment and provides theoretical support for clinicians to make medication prescriptions.It also presents a new perspective for investigating TCM medication patterns.

Objective To explore and organize the standards of rehabilitation big data. Methods The connotation and extension of rehabilitation big data were discussed based on International Classification of Functioning,Disability and Health(ICF)framework.Referring to the documents of Guidance on the analysis and use of routine health information systems rehabilitation module,Rehabilitation in health systems:guide for action,Rehabilitation indicator menu:a tool accompanying the Framework for Rehabilitation Monitoring and Evaluation(FRAME),and Data quality assurance.Module 1.Framework and metrics,the sources,patterns,clas-sification systems and coding standards were discussed under the ICF theory,and the metadata standards were ex-plored.The application and management of rehabilitation big data standards were discussed according to Nation-al Health Medical Big Data Standards,Security and Service Management Measures(Trial). Results The rehabilitation big data included rehabilitation service data and personal health data,coming from population-based and institution-based data,covering macro,meso and micro levels.The pattern of rehabilitation data flow corresponded to the interaction and source of the entire process of rehabilitation service,to organize and manage rehabilitation big data.The classification system included object classes,object feature classes,participant role classes,relationship classes,and activity and event classes,each of which was further subdivided into subcatego-ries to cover the entities,features,roles,relationships and activities involved in the rehabilitation process.The metadata standards included three levels:core,general and specialized metadata,ensuring standardized manage-ment,sharing and interoperability of rehabilitation data. Conclusion This study delves into the standardization of rehabilitation big data based on the ICF framework,encompass-ing multiple dimensions such as the connotation and extension of rehabilitation big data,data sources,data mod-els,classification systems,coding standards,and metadata standards.The construction of a rehabilitation big data standard system involves standardization efforts in various aspects,including data content,data structure,data coding,and metadata.These standards not only adhere to the norms of data flow,but also take into account the complexity of data composition.This system aligns with health big data standards,ensuring data consistency,ac-curacy,and interoperability,thus providing a foundation for effective exchange and comparison between different data sources.The establishment of a rehabilitation big data standard system not only ensures the standardized pro-cessing of rehabilitation big data,but also lays a solid foundation for effective exchange between rehabilitation big data and other health data,as well as for the widespread application of rehabilitation big data.This provides crucial support for improving the quality and efficiency of rehabilitation services,ensuring that patients receive appropriate care,rehabilitation and support.It holds significant theoretical and practical implications for promot-ing the development of the rehabilitation field.

OBJECTIVE To review the current research progress on pharmacoeconomics evaluation related to spinal muscular atrophy （SMA）， in order to provide valuable insights for clinical treatment， screening and medical insurance payment decision- making. METHODS A computerized search was conducted across multiple databases including PubMed， Web of Science， Embase， Scopus， Cochrane Library， EBSCOhost， CNKI， VIP， CBM and Wanfang database as well as other important health technology assessment （HTA） websites， such as National Institute for Health and Care Research，International Society of Technology Assessment in Health Care， Agency for Healthcare Research and Quality， etc. The pharmacoeconomics evaluation studies related to SMA were collected from the inception to December 31st， 2023. The literature/reports were rigorously screened based on predefined inclusion and exclusion criteria by two researchers， and the essential information from the included literature/ reports was extracted using Excel 2019. The quality of the included literature/reports was evaluated by Consolidated Health Economic Evaluation Reporting Standards 2022. RESULTS Finally， 9 articles and 15 HTA reports were included， with overall good quality of literature， but poor quality of HTA reports. There were a total of 24 studies on the pharmacoeconomics evaluation of SMA， including treatment options such as nusinersen sodium， sovaprevir， risperidone， and best supportive therapy.The review results showed that nusinersen sodium was not cost-effective in the treatment of SMA； there was no consensus on the economic viability of treatment options such as risperidone and sovaprevir； newborn/prenatal screening combined withmedication therapy was cost-effective. CONCLUSIONS newborn/prenatal screening combined with SMA medication therapy demonstrates economic advantages. It is suggested to further investigate the cost-effectiveness of new SMA drugs and SMA screening in China， taking localization parameters and medical insurance prices into account， and gradually incorporate SMA screening into the scope of neonatal genetic disease detection， in order to alleviate the financial burden of patients’ families and healthcare systems.

Objective:The purpose of this study is to explore the clinical characteristics of Coronavirus Disease 2019 (COVID-19) in patients with type 2 diabetes mellitus (T2DM), and analyze the risk factors for adverse outcomes.Methods:2 052 patients diagnosed with COVID-19 who were hospitalized in Shanxi Bethune Hospital between December 1, 2022 and March 20, 2023 were included. They were divided into diabetes group ( n=70) and non-diabetes group ( n=1 982) according to the presence or absence of comorbid T2DM. The two groups were matched at 1:1 via propensity score matching. Clinical characteristics and laboratory examination results of the two groups were compared. According to the outcomes during hospitalization, the two groups were further divided into two subgroups respectively. Univariate analysis and subsequent binary Logistic regression was used to analyze the risk factors of adverse outcomes in patients with COVID-19 and type 2 diabetes. Results:After the propensity score matching, the most common comorbid condition in diabetes group and non-diabetes group was hypertension. The proportion of patients with severe or critical disease in diabetes group was higher compared with non-diabetes group. The levels of hemoglobin A1c (HbA1c), fasting blood glucose (FBG), blood urea, IL-4, IL-6, IL-10, IFN-γ and TNF-α were significantly higher in the diabetes group ( P<0.05). Logistic regression analysis within the diabetes group showed that hypertension ( OR=3.640, 95% CI: 3.156 to 4.290), FBG>11 mmol/L ( OR=3.283, 95% CI: 1.416 to 7.611), HbA1c>10% ( OR=2.718, 95% CI: 1.024 to 7.213) were independent risk factors for adverse outcomes in patients with COVID-19 and type 2 diabetes(all P<0.05). Conclusions:Compared with the non-diabetes group, patients with COVID-19 and T2DM have worse inflammatory response and higher levels of inflammatory cytokines. The elevated levels of FBG and HbA1c are related to the adverse outcome in patients with COVID-19 and T2DM.