Research on the Solid-Liquid Composite Casting Process of Incoloy825/P110 Steel Composite Pipe.

Bimetallic composites have a wide range of application prospects in various industries. Different bonding temperatures, as one of the influencing factors, directly affect the bonding effectiveness as well as the performance and application of the materials. Using metallurgical bonding techniques ensures a strong bond at the interface of bimetallic materials, resulting in high-quality composite pipe billets. This paper describes an Incoloy825/P110 steel bimetal composite material made by the solid-liquid composite method. By utilizing ProCAST 14.5 software for simulation and deriving theoretical formulas, an initial range of temperatures for bimetallic preparation has been tentatively determined. And this temperature range will be utilized for on-site experiments to prepare bimetallic samples. After the preparation process is completed, samples will be selected. The influence of the external mold temperature on the interface bonding of Incoloy825/P110 steel solid-liquid composite material is studied using an ultra-depth three-dimensional morphology microscope and a scanning electron microscope. Through research, the optimal preheating temperature range for the solid-liquid composite outer mold of Incoloy825/P110 bimetallic composite material has been determined. The casting temperature of the inner mold has a significant impact on the interface bonding of this bimetal composite material. As the casting temperature of the inner mold increases, the interface thickness gradually increases. At lower temperatures, the interface thickness is lower and the bonding is poorer. At higher temperatures, melting may occur, leading to coarse grains at the interface. When the temperatures of the inner and outer molds are within a certain range, a new phase appears at the interface. Indeed, it increases the strength of the interface bonding. Due to co-melting of the bimetal near the interface, element migration occurs, resulting in increased Ni and Cr content at the interface and enhanced corrosion resistance.

Associations Between Neuroinflammation-Related Conditions and Alzheimer's Disease: A Study of US Insurance Claims Data.

Background: Early detection of Alzheimer's disease (AD) is a key component for the success of the recently approved lecanemab and aducanumab. Patients with neuroinflammation-related conditions are associated with a higher risk for developing AD. Objective: Investigate the incidence of AD among patients with neuroinflammation-related conditions including epilepsy, hemorrhage stroke, multiple sclerosis (MS), and traumatic brain injury (TBI). Methods: We used Optum's de-identified Clinformatics Data Mart Database (CDM). We derived covariate-matched cohorts including patients with neuroinflammation-related conditions and controls without the corresponding condition. The matched cohorts were: 1) patients with epilepsy and controls (N = 67,825 matched pairs); 2) patients with hemorrhage stroke and controls (N = 81,510 matched pairs); 3) patients with MS and controls (N = 9,853 matched pairs); and 4) patients TBI and controls (N = 104,637 matched pairs). We used the Cox model to investigate the associations between neuroinflammation-related conditions and AD. Results: We identified that epilepsy, hemorrhage stroke, and TBI were associated with increased risks of AD in both males and females (hazard ratios [HRs]≥1.74, p < 0.001), as well as in gender- and race-conscious subpopulations (HRs≥1.64, p < 0.001). We identified that MS was associated with increased risks of AD in both males and females (HRs≥1.47, p≤0.004), while gender- and race-conscious subgroup analysis shown mixed associations. Conclusions: Patients with epilepsy, hemorrhage stroke, MS, and/or TBI are associated with a higher risk of developing AD. More attention on cognitive status should be given to older patients with these conditions.

Sildenafil as a Candidate Drug for Alzheimer's Disease: Real-World Patient Data Observation and Mechanistic Observations from Patient-Induced Pluripotent Stem Cell-Derived Neurons.

Background: Alzheimer's disease (AD) is a chronic neurodegenerative disease needing effective therapeutics urgently. Sildenafil, one of the approved phosphodiesterase-5 inhibitors, has been implicated as having potential effect in AD. Objective: To investigate the potential therapeutic benefit of sildenafil on AD. Methods: We performed real-world patient data analysis using the MarketScan® Medicare Supplemental and the Clinformatics® databases. We conducted propensity score-stratified analyses after adjusting confounding factors (i.e., sex, age, race, and comorbidities). We used both familial and sporadic AD patient induced pluripotent stem cells (iPSC) derived neurons to evaluate the sildenafil's mechanism-of-action. Results: We showed that sildenafil usage is associated with reduced likelihood of AD across four new drug compactor cohorts, including bumetanide, furosemide, spironolactone, and nifedipine. For instance, sildenafil usage is associated with a 54% reduced incidence of AD in MarketScan® (hazard ratio [HR] = 0.46, 95% CI 0.32- 0.66) and a 30% reduced prevalence of AD in Clinformatics® (HR = 0.70, 95% CI 0.49- 1.00) compared to spironolactone. We found that sildenafil treatment reduced tau hyperphosphorylation (pTau181 and pTau205) in a dose-dependent manner in both familial and sporadic AD patient iPSC-derived neurons. RNA-sequencing data analysis of sildenafil-treated AD patient iPSC-derived neurons reveals that sildenafil specifically target AD related genes and pathobiological pathways, mechanistically supporting the beneficial effect of sildenafil in AD. Conclusions: These real-world patient data validation and mechanistic observations from patient iPSC-derived neurons further suggested that sildenafil is a potential repurposable drug for AD. Yet, randomized clinical trials are warranted to validate the causal treatment effects of sildenafil in AD.

Artificial intelligence and open science in discovery of disease-modifying medicines for Alzheimer's disease.

The high failure rate of clinical trials in Alzheimer's disease (AD) and AD-related dementia (ADRD) is due to a lack of understanding of the pathophysiology of disease, and this deficit may be addressed by applying artificial intelligence (AI) to "big data" to rapidly and effectively expand therapeutic development efforts. Recent accelerations in computing power and availability of big data, including electronic health records and multi-omics profiles, have converged to provide opportunities for scientific discovery and treatment development. Here, we review the potential utility of applying AI approaches to big data for discovery of disease-modifying medicines for AD/ADRD. We illustrate how AI tools can be applied to the AD/ADRD drug development pipeline through collaborative efforts among neurologists, gerontologists, geneticists, pharmacologists, medicinal chemists, and computational scientists. AI and open data science expedite drug discovery and development of disease-modifying therapeutics for AD/ADRD and other neurodegenerative diseases.

Study of the molecular structure of proteins in fermented Maize-Soybean meal-based rations based on FTIR spectroscopy.

This research investigates the dynamic alterations that occur in protein molecular structure during the fermentation process of feed. Fourier transform infrared spectroscopy (FTIR), coupled with deconvolution, second derivative and curve-fitting methodologies, was employed to comparatively analyse the protein molecular structures in fermented feed. At the 48-h fermentation mark, the α-helix and ß-sheet contents reached their peaks, while the random coil and ß-turn contents were at their lowest. Simultaneously, the ß-sheet/α-helix ratio was minimized. FTIR spectroscopy emerged as a comprehensive tool, revealing the nuanced changes in molecular structure throughout the fermentation process of corn-soybean meal feed. When integrated with spectral quantitative analysis, it provides a novel perspective for evaluating the nutritional value of fermented feed.

Trends in Neonatal Opioid Withdrawal Syndrome and Opioid Exposure Diagnoses Among Infants With Private Health Insurance, 2016-2021: Introduction of the P04.14 ICD-10-CM Code.

OBJECTIVE: The opioid epidemic has led to a surge in diagnoses of neonatal opioid withdrawal syndrome (NOWS). Many states track the incidence of NOWS by using the P96.1 International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) code for "neonatal withdrawal symptoms from maternal use of drugs of addiction." In October 2018, an ICD-10-CM code for neonatal opioid exposure (P04.14) was introduced. This code can be used when an infant is exposed to opioids in utero but does not have clinically significant withdrawal symptoms. We analyzed the effect of the P04.14 code on the incidence rate of NOWS (P96.1) and "other" neonatal drug exposure diagnoses (P04.49). METHODS: We used private health insurance data collected for infants in the United States from the first quarter of 2016 through the third quarter of 2021 to describe incidence rates for each code over time and examine absolute and percentage changes before and after the introduction of code P04.14. RESULTS: The exclusive use of code P96.1 declined from an incidence rate per 1000 births of 1.08 in 2016-2018 to 0.70 in 2019-2021, a -35.7% (95% CI, -47.6% to -23.8%) reduction. Use of code P04.49 only declined from an incidence rate of 2.34 in 2016-2018 to 1.64 in 2019-2021, a -30.0% (95% CI, -36.4% to -23.7%) reduction. Use of multiple codes during the course of treatment increased from an average incidence per 1000 births of 0.56 in 2016-2018 to 0.79 in 2019-2021, a 45.5% (95% CI, 24.8%-66.1%) increase. CONCLUSION: The introduction of ICD-10-CM code P04.14 altered the use of other neonatal opioid exposure codes. The use of multiple codes increased, indicating that some ambiguity may exist about which ICD-10-CM code is most appropriate for a given set of symptoms.

Application of an Innovative Data Mining Approach Towards Safe Polypharmacy Practice in Older Adults.

INTRODUCTION: Polypharmacy is common and is associated with higher risk of adverse drug event (ADE) among older adults. Knowledge on the ADE risk level of exposure to different drug combinations is critical for safe polypharmacy practice, while approaches for this type of knowledge discovery are limited. The objective of this study was to apply an innovative data mining approach to discover high-risk and alternative low-risk high-order drug combinations (e.g., three- and four-drug combinations). METHODS: A cohort of older adults (≥ 65 years) who visited an emergency department (ED) were identified from Medicare fee-for-service and MarketScan Medicare supplemental data. We used International Classification of Diseases (ICD) codes to identify ADE cases potentially induced by anticoagulants, antidiabetic drugs, and opioids from ED visit records. We assessed drug exposure data during a 30-day window prior to the ED visit dates. We investigated relationships between exposure of drug combinations and ADEs under the case-control setting. We applied the mixture drug-count response model to identify high-order drug combinations associated with an increased risk of ADE. We conducted therapeutic class-based mining to reveal low-risk alternative drug combinations for high-order drug combinations associated with an increased risk of ADE. RESULTS: We investigated frequent high-order drug combinations from 8.4 million ED visit records (5.1 million from Medicare data and 3.3 million from MarketScan data). We identified 5213 high-order drug combinations associated with an increased risk of ADE by controlling the false discovery rate at 0.01. We identified 1904 high-order, high-risk drug combinations had potential low-risk alternative drug combinations, where each high-order, high-risk drug combination and its corresponding low-risk alternative drug combination(s) have similar therapeutic classes. CONCLUSIONS: We demonstrated the application of a data mining technique to discover high-order drug combinations associated with an increased risk of ADE. We identified high-risk, high-order drug combinations often have low-risk alternative drug combinations in similar therapeutic classes.

Identifying Genes Associated with Alzheimer's Disease Using Gene-Based Polygenic Risk Score.

BACKGROUND: Except APOE, Alzheimer's disease (AD) associated genes identified in recent large-scale genome-wide association studies (GWAS) had small effects and explained a small portion of heritability. Many AD-associated genes have even smaller effects thereby sub-threshold p-values in large-scale GWAS and remain to be identified. For some AD-associated genes, drug targeting them may have limited efficacies due to their small effect sizes. OBJECTIVE: The purpose of this study is to identify AD-associated genes with sub-threshold p-values and prioritize drugs targeting AD-associated genes that have large efficacies. METHODS: We developed a gene-based polygenic risk score (PRS) to identify AD genes. It was calculated using SNPs located within genes and having the same directions of effects in different study cohorts to exclude cohort-specific findings and false positives. Gene co-expression modules and protein-protein interaction networks were used to identify AD-associated genes that interact with multiple other genes, as drugs targeting them have large efficacies via co-regulation or interactions. RESULTS: Gene-based PRS identified 389 genes with 164 of them not previously reported as AD-associated. These 389 genes explained 56.12% -97.46% SNP heritability; and they were enriched in brain tissues and 164 biological processes, most of which are related to AD and other neurodegenerative diseases. We prioritized 688 drugs targeting 64 genes that were in the same co-expression modules and/or PPI networks. CONCLUSIONS: Gene-based PRS is a cost-effective way to identify AD-associated genes without substantially increasing the sample size. Co-expression modules and PPI networks can be used to identify drugs having large efficacies.

The INGENIOUS trial: Impact of pharmacogenetic testing on adverse events in a pragmatic clinical trial.

Adverse drug events (ADEs) account for a significant mortality, morbidity, and cost burden. Pharmacogenetic testing has the potential to reduce ADEs and inefficacy. The objective of this INGENIOUS trial (NCT02297126) analysis was to determine whether conducting and reporting pharmacogenetic panel testing impacts ADE frequency. The trial was a pragmatic, randomized controlled clinical trial, adapted as a propensity matched analysis in individuals (N = 2612) receiving a new prescription for one or more of 26 pharmacogenetic-actionable drugs across a community safety-net and academic health system. The intervention was a pharmacogenetic testing panel for 26 drugs with dosage and selection recommendations returned to the health record. The primary outcome was occurrence of ADEs within 1 year, according to modified Common Terminology Criteria for Adverse Events (CTCAE). In the propensity-matched analysis, 16.1% of individuals experienced any ADE within 1-year. Serious ADEs (CTCAE level ≥ 3) occurred in 3.2% of individuals. When combining all 26 drugs, no significant difference was observed between the pharmacogenetic testing and control arms for any ADE (Odds ratio 0.96, 95% CI: 0.78-1.18), serious ADEs (OR: 0.91, 95% CI: 0.58-1.40), or mortality (OR: 0.60, 95% CI: 0.28-1.21). However, sub-group analyses revealed a reduction in serious ADEs and death in individuals who underwent pharmacogenotyping for aripiprazole and serotonin or serotonin-norepinephrine reuptake inhibitors (OR 0.34, 95% CI: 0.12-0.85). In conclusion, no change in overall ADEs was observed after pharmacogenetic testing. However, limitations incurred during INGENIOUS likely affected the results. Future studies may consider preemptive, rather than reactive, pharmacogenetic panel testing.

Comparison of different risk stratifications for gastric cancer and establishing a simplified risk-scoring model based on the Kyoto classification.

BACKGROUND AND AIM: The study aims to assess the value of different risk stratifications in diagnosing early gastric cancer (GC) and explore risk factors based on Kyoto gastritis classification. METHODS: This study was a single-centered cross-sectional study; all epidemiological data and endoscopic findings were obtained prospectively. To evaluate the proportion of GC in each risk stratification and to compare the diagnostic performance of different methods using the receiver operating characteristic curve, univariable and multivariable analyses were used to explore the correlation between endoscopic findings and GC. RESULTS: A total of 240 subjects were enrolled, and the diagnostic efficacy of the Kyoto Classification Score was similar to Operative Link on Gastric Intestinal Metaplasia Assessment (OLGIM) stage, and the accuracy was higher than that of the Japanese scoring system and OLGA stage. Moderate atrophy (odds ratio [OR] = 3.52, 95% confidence interval [CI]: 1.52-8.16), severe atrophy (OR = 4.96, 95% CI: 1.75-14.04), map-like redness (OR = 9.89, 95% CI: 1.16-84.15), and xanthelasma (OR = 3.57, 95% CI: 1.15-11.15) were independent risk factors for GC. The simplified Kyoto classification (area under the receiver operating characteristic [AUROC] = 0.76, P = 0.58) based on multivariable analysis demonstrated favorable diagnostic value compared with traditional Kyoto classification score (AUROC = 0.74). CONCLUSIONS: This study confirms the value of the Kyoto classification score and the OLGIM stage in the risk stratification of GC. Simplified Kyoto classification is also promising in risk assessment of GC but still requires validation in the population.

Current evidence, clinical applications, and future directions of transcranial magnetic stimulation as a treatment for ischemic stroke.

Transcranial magnetic stimulation (TMS) is a non-invasive brain neurostimulation technique that can be used as one of the adjunctive treatment techniques for neurological recovery after stroke. Animal studies have shown that TMS treatment of rats with middle cerebral artery occlusion (MCAO) model reduced cerebral infarct volume and improved neurological dysfunction in model rats. In addition, clinical case reports have also shown that TMS treatment has positive neuroprotective effects in stroke patients, improving a variety of post-stroke neurological deficits such as motor function, swallowing, cognitive function, speech function, central post-stroke pain, spasticity, and other post-stroke sequelae. However, even though numerous studies have shown a neuroprotective effect of TMS in stroke patients, its possible neuroprotective mechanism is not clear. Therefore, in this review, we describe the potential mechanisms of TMS to improve neurological function in terms of neurogenesis, angiogenesis, anti-inflammation, antioxidant, and anti-apoptosis, and provide insight into the current clinical application of TMS in multiple neurological dysfunctions in stroke. Finally, some of the current challenges faced by TMS are summarized and some suggestions for its future research directions are made.

Mirikizumab-Induced Transcriptome Changes in Ulcerative Colitis Patient Biopsies at Week 12 Are Maintained Through Week 52.

INTRODUCTION: Mirikizumab, an anti-interleukin-23p19 monoclonal antibody, demonstrated efficacy in phase 2 and 3 randomized clinical trials of patients with moderate-to-severe ulcerative colitis (UC). Previous results have shown that 12 weeks of mirikizumab treatment downregulated transcripts associated with UC disease activity and tumor necrosis factor inhibitor resistance. We assessed week-52 gene expression from week-12 responders receiving mirikizumab or placebo. METHODS: In the phase 2 AMAC study (NCT02589665), mirikizumab-treated patients achieving week-12 clinical response were rerandomized to mirikizumab 200 mg subcutaneous every 4 or 12 weeks through week 52 (N = 31). Week-12 placebo responders continued placebo through week 52 (N = 7). The limma R package clustered transcript changes in colonic mucosa biopsies from baseline to week 12 into differentially expressed genes (DEGs). Among DEGs, similarly expressed genes (DEGSEGs) maintaining week-12 expression through week 52 were identified. RESULTS: Of 89 DEGSEGs, 63 (70.8%) were present only in mirikizumab induction responders, 5 (5.6%) in placebo responders, and 21 (23.6%) in both. Week-12 magnitudes and week-52 consistency of transcript changes were greater in mirikizumab than in placebo responders (log2FC > 1). DEGSEG clusters (from 84 DEGSEGs identified in mirikizumab and mirikizumab/placebo responders) correlated to modified Mayo score (26/84 with Pearson correlation coefficient [PCC] >0.5) and Robarts Histopathology Index (55/84 with PCC >0.5), sustained through week 52. DISCUSSION: Mirikizumab responders had broader, more sustained transcriptional changes of greater magnitudes at week 52 vs placebo. Mirikizumab responder DEGSEGs suggest a distinct molecular healing pathway associated with mirikizumab interleukin-23 inhibition. The cluster's correlation with disease activity illustrates relationships between clinical, endoscopic, and molecular healing in UC.

DEPOT: graph learning delineates the roles of cancers in the progression trajectories of chronic kidney disease using electronic medical records.

Chronic kidney disease (CKD) is a common, complex, and heterogeneous disease impacting aging populations. Determining the landscape of disease progression trajectories from midlife to senior age in a real-world context allows us to better understand the progression of CKD, the heterogeneity of progression patterns among the risk population, and the interactions with other clinical conditions like cancers. In this study, we use electronic health records (EHRs) to outline the CKD progression trajectory roadmap for the Wake Forest Baptist Medical Center (WFBMC) patient population. We establish an EHR cohort (n = 79,434) with patients' health status identified by 18 Essential Clinical Indices across 508,732 clinical encounters. We develop the DisEase PrOgression Trajectory (DEPOT) approach to model CKD progression trajectories and individualize clinical decision support. The DEPOT is an evidence-driven, graph-based clinical informatics approach that addresses the unique challenges in longitudinal EHR data by systematically using the graph artificial intelligence (graph-AI) model for representation learning and reverse graph embedding for trajectory reconstruction. Moreover, DEPOT includes a prediction model to assign new patients along the progression trajectory. We successfully establish the EHR-based CKD progression trajectories with DEPOT in the WFUBMC cohort. We annotate the trajectories with clinical features, including kidney function, age, and other indices, including cancer. This CKD progression trajectory roadmap reveals diverse kidney failure pathways associated with different clinical conditions. Specifically, we have identified one high-risk trajectory and two low-risk trajectories. Switching pathways from low-risk trajectories to the high-risk one is associated with accelerated decline in kidney function. On this roadmap, high-risk patients are enriched in the skin and GU cancers, which differs from low-risk patients, suggesting fundamentally different disease progression mechanisms. Overall, the CKD progression trajectory roadmap reveals novel diverse renal failure pathways in type 2 diabetes mellitus and highlights disease progression patterns associated with cancer phenotypes.

An Early Adverse Drug Event Detection Approach with False Discovery Rate Control.

Adverse drug event (ADE) is a significant challenge in clinical practice. Many ADEs have not been identified timely after the approval of the corresponding drugs. Despite the use of drug similarity network demonstrates early success on improving ADE detection, false discovery rate (FDR) control remains unclear in its application. Additionally, performance of early ADE detection has not been explicitly investigated under the time-to-event framework. In this manuscript, we propose to use the drug similarity based posterior probability of null hypothesis for early ADE detection. The proposed approach is also able to control FDR for monitoring a large number of ADEs of multiple drugs. The proposed approach outperforms existing approaches on mining labeled ADEs in the US FDA's Adverse Event Reporting System (FAERS) data, especially in the first few years after the drug initial reporting time. Additionally, the proposed approach is able to identify more labeled ADEs and has significantly lower time to ADE detection. In simulation study, the proposed approach demonstrates proper FDR control, as well as has better true positive rate and an excellent true negative rate. In our exemplified FAERS analysis, the proposed approach detects new ADE signals and identifies ADE signals in a timelier fashion than existing approach. In conclusion, the proposed approach is able to both reduce the time and improve the FDR control for ADE detection.

Sleep Profiles in Eating Disorders: A Scientometric Study on 50 Years of Clinical Research.

Sleep and diet are essential for maintaining physical and mental health. These two factors are closely intertwined and affect each other in both timing and quality. Eating disorders, including anorexia nervosa and bulimia nervosa, are often accompanied by different sleep problems. In modern society, an increasing number of studies are being conducted on the relationship between eating disorders and sleep. To gain a more comprehensive understanding of this field and highlight influential papers as well as the main research domains in this area, a scientometric approach was used to review 727 publications from 1971 to 2023. All documents were retrieved from Scopus through the following string "TITLE-ABS (("sleep" OR "insomnia") AND ("anorexia nervosa" OR "bulimia nervosa" OR "binge eating" OR "eating disorder*") AND NOT "obes*") AND (LIMIT-TO (LANGUAGE, "English"))". A document co-citation analysis was applied to map the relationship between relevant articles and their cited references as well as the gaps in the literature. Nine publications on sleep and eating disorders were frequently cited, with an article by Vetrugno and colleagues on nocturnal eating being the most impactful in the network. The results also indicated a total of seven major thematic research clusters. The qualitative inspection of clusters strongly highlights the reciprocal influence of disordered eating and sleeping patterns. Researchers have modelled this reciprocal influence by taking into account the role played by pharmacological (e.g., zolpidem, topiramate), hormonal (e.g., ghrelin), and psychological (e.g., anxiety, depression) factors, pharmacological triggers, and treatments for eating disorders and sleep problems. The use of scientometric perspectives provides valuable insights into the field related to sleep and eating disorders, which can guide future research directions and foster a more comprehensive understanding of this important area.

Tongue acupuncture for the treatment of post-stroke dysphagia: a meta-analysis of randomized controlled trials.

Objectives: Post-stroke dysphagia is the most common neurological impairment after stroke. The swallowing process is controlled by a network made up of the cerebral cortex, subcortical area, and brainstem structure. The disruption of the swallowing network after stroke leads to dysphagia. The affected swallowing muscles after stroke mainly include the laryngeal muscles (suprahyoid muscle and thyrohyoid muscle) and infrahyoid muscle. These muscles experience kinematic effects and muscle strength weakens, resulting in reduced movement in the swallowing process. Acupuncture can change the excitability of cerebral cortical nerve cells, promote the recovery of neurological function, and enhance neuromuscular excitability, ultimately improving the control of swallowing-related nerves and muscles and promoting swallowing functional recovery. In this meta-analysis, we systematically evaluate the clinical efficacy of acupuncture in the treatment of post-stroke dysphagia. Methods: Randomized controlled trials of tongue acupuncture therapy for post-stroke dysphagia were searched and selected from seven electronic databases (PubMed, CBM, Cochrane, Embase, CNKI, VPCS, and Wan fang). The Cochrane Collaboration tool was used to conduct methodological quality assessment. Rev. Man 5.4 software was utilized to perform data analysis. Results: A total of 15 studies with 1,094 patients were included. Meta-analysis Showed that WST score WST score (MD = -0.56, 95% CI (-1.23, 0.12), Z = 1.62, p < 0.00001), SSA score (MD = -1.65, 95% CI (-2.02, -1.28), Z = 8.77, p < 0.00001). These results suggested that the treatment group (tongue acupuncture or tongue acupuncture combined with other therapies) was superior to the control group in reducing WST scores and SSA scores. The clinical efficacy of the tongue acupuncture group was better compared with the control group (MD = 3.83, 95% CI (2.61, 5.62), Z = 6.88, p < 0.00001). Conclusion: The meta-analysis showed that the total effective rate of patients with dysphagia after stroke in the treatment group (acupuncture, tongue acupuncture, and acupuncture combined with other therapy) was higher than that in the control group. These results indicated that acupuncture, tongue acupuncture, and acupuncture combined with other therapy can improve post-stroke dysphagia.

Risk of lymph node metastasis and feasibility of endoscopic submucosal dissection in undifferentiated-type early gastric cancer.

BACKGROUND: Whether endoscopic submucosal dissection (ESD) applies to undifferentiated-type early gastric cancer (UEGC) remains controversial. We aimed to analyze the risk factors for lymph node metastasis (LNM) in UEGC and evaluate the feasibility of ESD. METHODS: This study included 346 patients with UEGC who underwent curative gastrectomy between January 2014 and December 2021. Univariate and multivariate analyses of the correlation between clinicopathological features and LNM were conducted, and the risk factors for exceeding the expanded ESD indications were evaluated. RESULTS: The overall LNM rate in UEGC was 19.94%. Among the preoperatively assessable factors, submucosal invasion (odds ratio [OR] = 4.77, 95% confidence interval [CI]: 2.14-10.66) and > 2 cm(OR = 2.49, 95% CI: 1.20-5.15) were independent risk factors for LNM, while postoperative independent risk factors were > 2 cm (OR = 3.35, 95% CI: 1.02-5.40) and lymphovascular invasion(OR = 13.21, 95% CI: 5.18-33.70). Patients who met the expanded indications had a low LNM risk (4.1%). Additionally, tumors located in the cardia (P = 0.03), non-elevated type (P < 0.01) were independent risk factors for exceeding the expanded indications in UEGC. CONCLUSIONS: ESD may be applicable for UEGC meeting the expanded indications, and preoperative evaluation should be cautious when the lesion is non-elevated type or located in the cardia. TRIAL REGISTRATION: Chinese Clinical Trial Registry (12/05/2022 ChiCTR2200059841 ).