BACKGROUND: Degenerative cervical myelopathy (DCM), the predominant cause of spinal cord dysfunction among adults, exhibits diverse interrelated symptoms and significant heterogeneity in clinical presentation. This study sought to use machine learning-based clustering algorithms to identify distinct patient clinical profiles and functional trajectories following surgical intervention.
METHODS: In this study, we applied k-means and latent profile analysis (LPA) to identify patient phenotypes, using aggregated data from three major DCM trials. The combination of Nurick score, NDI (neck disability index), neck pain, as well as motor and sensory scores facilitated clustering. Goodness-of-fit indices were used to determine the optimal cluster number. ANOVA and post hoc Tukey\'s test assessed outcome differences, while multinomial logistic regression identified significant predictors of group membership.
RESULTS: A total of 1047 patients with DCM (mean [SD] age: 56.80 [11.39] years, 411 [39%] females) had complete one year outcome assessment post-surgery. Latent profile analysis identified four DCM phenotypes: \"severe multimodal impairment\" (n = 286), \"minimal impairment\" (n = 116), \"motor-dominant\" (n = 88) and \"pain-dominant\" (n = 557) groups. Each phenotype exhibited a unique symptom profile and distinct functional recovery trajectories. The \"severe multimodal impairment group\", comprising frail elderly patients, demonstrated the worst overall outcomes at one year (SF-36 PCS mean [SD]: 40.01 [9.75]; SF-36 MCS mean [SD], 46.08 [11.50]) but experienced substantial neurological recovery post-surgery (ΔmJOA mean [SD]: 3.83 [2.98]). Applying the k-means algorithm yielded a similar four-class solution. A higher frailty score and positive smoking status predicted membership in the \"severe multimodal impairment\" group (OR 1.47 [95% CI 1.07-2.02] and 1.58 [95% CI 1.25-1.99, respectively]), while undergoing anterior surgery and a longer symptom duration were associated with the \"pain-dominant\" group (OR 2.0 [95% CI 1.06-3.80] and 3.1 [95% CI 1.38-6.89], respectively).
CONCLUSIONS: Unsupervised learning on multiple clinical metrics predicted distinct patient phenotypes. Symptom clustering offers a valuable framework to identify DCM subpopulations, surpassing single patient reported outcome measures like the mJOA.
BACKGROUND: No funding was received for the present work. The original studies were funded by AO Spine North America.

Objective: To utilize routinely available clinical parameters to uncover the clinical features of different clusters in patients with chronic rhinosinusitis with nasal polyp (CRSwNP) through unsupervised clustering analysis. Methods: The clinical data from 155 CRSwNP patients undergoing nasal endoscopic surgery at Renmin Hospital of Wuhan University from 2021 to 2023 were prospectively collected, including 112 males and 43 females, aged from 7 to 87 years. Unsupervised clustering analysis was conducted using various clinical parameters, including age, gender, smoking and drinking history, local eosinophil (EOS) and neutrophil (NEU) counts, comorbid allergic rhinitis (AR), comorbid asthma, recurrence status, serum-specific IgE, total IgE, cytokine levels, peripheral blood EOS count and percentage, Lund-Mackay CT score, the ratio of CT scores for the maxillary sinus and ethmoid sinus (E/M ratio), visual analogue scale (VAS) score, Lund-Kennedy endoscopic score, and other common clinical indicators to elucidate the clinical characteristics of each cluster. Statistical analysis was conducted using GraphPad Prism 9.5 software. Results: Hierarchical clustering analysis identified four main clusters (Cluster A1-A4), showcasing distinct characteristics such as mild nasal polyps with higher peripheral blood cytokines levels, nasal polyps accompanied by allergies and asthma, a subtype of nasal polyps with high recurrence rates dominated by neutrophils, and nasal polyps with high eosinophil levels. Further subset clustering revealed two clusters of mild polyps (Cluster B1-B2) featuring high cytokine expression and comorbid AR; and two clusters of severe polyps (Cluster B3-B4) presented with severe symptoms, high Lund-Mackay CT score, and high Lund-Kennedy endoscopic score. Variations between Cluster B3 and B4 included symptom complexity, the degree of eosinophil infiltration, and the probability of comorbid asthma. Further clustering analysis for eosinophilic nasal polyps revealed a cluster characterized by highly neutrophilic infiltration and recurrent nasal polyps. The comprehensive analysis of multi-index correlations demonstrated valuable insights into the relationships between common clinical parameters of nasal polyps, providing valuable information for a deeper understanding of the pathogenesis of CRSwNP. Conclusion: The clustering analysis in this study categorizes CRSwNP patients into different clusters based on clinical features and disease outcomes, providing a new perspective for more precise clinical treatment strategies.
目的： 利用常规可用的临床标志物，通过对慢性鼻窦炎伴鼻息肉（CRSwNP）患者进行无监督聚类分析，揭示不同集群患者的临床特征。 方法： 收集2021—2023年于武汉大学人民医院接受鼻内镜手术治疗的155例CRSwNP患者（男112例，女43例，年龄7~87岁）的临床数据，包括年龄、性别、吸烟史、饮酒史、组织局部嗜酸粒细胞（EOS）计数及中性粒细胞（NEU）计数、共病变应性鼻炎（AR）、共病哮喘、是否复发、血清特异性免疫球蛋白E（IgE）、总IgE、细胞因子水平、外周血EOS计数及占比、Lund-Mackay CT评分、筛窦和上颌窦CT评分的比率（E/M比率）、视觉模拟量表（VAS）评分、Lund-Kennedy内镜评分等常见临床指标，进行无监督聚类分析，以明确每个群组对应的临床特征。采用GraphPad Prism 9.5软件对数据进行统计学分析。 结果： 通过临床核心指标的层次聚类分析，我们将患者分为4类（Cluster A1~A4），其主要特点是外周血细胞因子较高的轻症息肉、伴发过敏及哮喘的鼻息肉、高复发的NEU型鼻息肉亚组，以及高嗜酸性鼻息肉。核心指标及其子集的聚类结果展示了两类轻症息肉（Cluster B1~B2），其主要特点是细胞因子高表达、合并AR；以及两类重型息肉（Cluster B3~B4），临床表现为症状严重且Lund-Mackay CT评分、Lund-Kennedy内镜评分均高，其差异在于症状的复杂性、EOS浸润程度和伴发哮喘的概率。针对嗜酸性鼻息肉的进一步聚类分析，揭示了一类高度NEU浸润的复发性鼻息肉。多指标相关性分析的结果全面展示了鼻息肉常见临床参数之间的关联，为深入理解CRSwNP的临床表型提供了有价值的信息。 结论： 通过聚类分析，成功将CRSwNP患者分为不同亚型，深入探讨了它们在临床特征和疾病结果上的差异，为制定更精准的临床治疗方案提供了新的视角。.

Objective: To analyze the characteristics of patients with chronic rhinosinusitis (CRS) in the South China region based on pathological tissue biomarkers for regional comparison. Methods: The study population consisted of CRS in-patients in the First Affiliated Hospital of Sun Yat-sen University from October 2019 to June 2022. Among all the 181 cases, 123 of them were male and 58 were female, with an average age of 40. Retrospectively collected clinical data included demographic information, preoperative symptom scores, preoperative endoscopic images, preoperative paranasal sinus computed tomography scanning images, and inflammatory serological features. In addition, 52 variables of pathological tissue biomarkers including cytokines, chemokines and remodeling factors were collected for analysis. Cluster analysis was performed on the integrated data of training set through centroid-based clustering algorithm, and the inflammatory characteristics, post-operation control status, and airway diseases comorbidity of each endotype were analyzed. R project (version 4.2.2) was used in statistical analysis. Results: Cluster analysis divided 181 patients with CRS into 4 endotypes. Cluster 1 (n=101, 55.80%) showed a locally low inflammatory status. Cluster 2 (n=23, 12.71%) showed a mixed type of inflammation with predominantly neutrophilic inflammation and tissue remodeling. Cluster 3 (n=11, 6.08%) was characterized by type Ⅱ inflammation without tissue remodeling. Cluster 4 (n=46, 25.41%) was mainly characterized by type Ⅱ inflammation with tissue remodeling, showing higher comorbidity rate of asthma and allergic rhinitis. This cluster presented more severe symptoms, significant olfactory dysfunction, extensive overall inflammation based on objective examination results, a notable increase in total eosinophil count and proportion in peripheral blood, and the highest uncontrolled rate observed one year post-surgery. In comparison to other regions, the endotype classification of CRS in Southern China was characterized by a predominant pattern of locally low inflammatory status, a moderate level of type Ⅱ inflammation with tissue remodeling, and a lesser presence of neutrophilic inflammation. Conclusion: CRS distribution in Southern China is mainly characterized by low inflammatory endotype and type Ⅱ inflammation with tissue remodeling. The latter shows more severe clinical manifestations and higher uncontrol rate after surgery.
目的： 建立华南地区慢性鼻窦炎（CRS）内型分型模型，并与其他地区内型特征进行比较。 方法： 回顾性分析2019年10月至2022年6月就诊于中山大学附属第一医院的181例CRS患者的临床和随访信息（包括人口学资料、术前症状评分、鼻内镜资料、鼻窦影像学评分、血清学特征等19项临床数据），其中男性123例、女性58例，平均年龄40岁，并收集患者黏膜组织匀浆的52项生物标志物（如细胞因子、趋化因子和重塑因子等）检测数据。对训练集的生物标志物数据进行聚类分析，并对所得到的各内型分型的炎症特征、术后短期与长期控制情况、气道合并症发病率等结局变量进行分析。通过R软件（版本4.2.2）进行统计学分析。 结果： 聚类分析将181例CRS患者划分为4个分型。分型1（101例，55.80%）为局部低炎症水平型，分型2（23例，12.71%）为中性粒细胞性炎症为主的混合型炎症伴显著组织重塑型，分型3（11例，6.08%）为Ⅱ型炎症为主不伴显著组织重塑型，分型4（46例，25.41%）为Ⅱ型炎症为主伴显著组织重塑型。相较其他3型，分型4的哮喘和变应性鼻炎共病率高，症状更重，嗅觉减退更明显，CT与内镜评分结果提示鼻腔鼻窦整体炎症更广泛，外周血嗜酸粒细胞总数和比例显著升高，且术后1年的未控制率最高。相较其他地域，华南地区的CRS内型分型表现为局部低炎症型模式为主、Ⅱ型炎症型模式处于中等水平、中性粒细胞型炎症较少的特点。 结论： 华南地区CRS主要表现为局部低炎症型和Ⅱ型炎症伴显著组织重塑的内型特征，后者整体临床症状较严重，且术后控制情况较差。.

Children with autism spectrum disorder (ASD) often face challenges in early social communication skills, prompting the need for a detailed exploration of specific behaviors and their impact on cognitive and adaptive functioning. This study aims to address this gap by examining the developmental trajectories of early social communication skills in preschoolers with ASD aged 18-60 months, comparing them to age-matched typically developing (TD) children. Utilizing the early social communication scales (ESCS), the research employs a longitudinal design to capture changes over time. We apply a principal component analysis (PCA) to ESCS variables to identify underlying components, and cluster analysis to identify subgroups based on preverbal communication profiles. The results reveal consistent differences in early social communication skills between ASD and TD children, with ASD children exhibiting reduced skills. PCA identifies two components, distinguishing objects-directed behaviors and social interaction-directed behaviors. Cluster analysis identifies three subgroups of autistic children, each displaying specific communication profiles associated with distinct cognitive and adaptive functioning trajectories. In conclusion, this study provides a nuanced understanding of early social communication development in ASD, emphasizing the importance of low-level behaviors. The identification of subgroups and their unique trajectories contributes to a more comprehensive understanding of ASD heterogeneity. These findings underscore the significance of early diagnosis, focusing on specific behaviors predicting cognitive and adaptive functioning outcomes. The study encourages further research to explore the sequential development of these skills, offering valuable insights for interventions and support strategies.

OBJECTIVE: Gut microbiota is closely related to the occurrence and development of colorectal cancer (CRC). However, the differences in bacterial co-abundance groups (CAGs) between tumor tissue (TT) and normal tissue (NT), as well as their associations with clinical features, are needed to be clarified.
METHODS: Bacterial 16 S rRNA sequencing was performed by using TT samples and NT samples of 251 patients with colorectal cancer. Microbial diversity, taxonomic characteristics, microbial composition, and functional pathways were compared between TT and NT. Hierarchical clustering was used to construct CAGs.
RESULTS: Four CAGs were grouped in the hierarchical cluster analysis. CAG 2, which was mainly comprised of pathogenic bacteria, was significantly enriched in TT samples (2.27% in TT vs. 0.78% in NT, p < 0.0001). CAG 4, which was mainly comprised of non-pathogenic bacteria, was significantly enriched in NT samples (0.62% in TT vs. 0.79% in NT, p = 0.0004). In addition, CAG 2 was also significantly associated with tumor microsatellite instability (13.2% in unstable vs. 2.0% in stable, p = 0.016), and CAG 4 was positively correlated with the level of CA199 (r = 0.17, p = 0.009).
CONCLUSIONS: Our research will deepen our understanding of the interactions among multiple bacteria and offer insights into the potential mechanism of NT to TT transition.

This paper introduces a novel approach to modeling malaria incidence in Nigeria by integrating clustering strategies with regression modeling and leveraging meteorological data. By decomposing the datasets into multiple subsets using clustering techniques, we increase the number of explanatory variables and elucidate the role of weather in predicting different ranges of incidence data. Our clustering-integrated regression models, accompanied by optimal barriers, provide insights into the complex relationship between malaria incidence and well-established influencing weather factors such as rainfall and temperature.We explore two models. The first model incorporates lagged incidence and individual-specific effects. The second model focuses solely on weather components. Selection of a model depends on decision-makers priorities. The model one is recommended for higher predictive accuracy. Moreover, our findings reveal significant variability in malaria incidence, specific to certain geographic clusters and beyond what can be explained by observed weather variables alone.Notably, rainfall and temperature exhibit varying marginal effects across incidence clusters, indicating their differential impact on malaria transmission. High rainfall correlates with lower incidence, possibly due to its role in flushing mosquito breeding sites. On the other hand, temperature could not predict high-incidence cases, suggesting that other factors other than temperature contribute to high cases.Our study addresses the demand for comprehensive modeling of malaria incidence, particularly in regions like Nigeria where the disease remains prevalent. By integrating clustering techniques with regression analysis, we offer a nuanced understanding of how predetermined weather factors influence malaria transmission. This approach aids public health authorities in implementing targeted interventions. Our research underscores the importance of considering local contextual factors in malaria control efforts and highlights the potential of weather-based forecasting for proactive disease management.

UNASSIGNED: After conducting a comprehensive literature search of two medical electronic databases, PubMed and Embase, as well as two citation databases, Web of Science Core Collections (WoS) and Scopus, we aimed to conduct an Altmetric and Scientometric analysis of the History of Medicine literature in medical research.
UNASSIGNED: The following software tools were used for analyzing the retrieved records from PubMed and Embase databases and conducting a collaboration analysis to identify the countries involved in scientific medical papers, as well as clustering keywords to reveal the trend of History of Medicine research for the future. These software tools (VOSviewer 1.6.18 and Spss 16) allowed the researchers to visualize bibliometric networks, perform statistical analysis, and identify patterns and trends in the data.
UNASSIGNED: Our analysis revealed 53,771 records from PubMed and 54,405 records from EMBASE databases retrieved in the field of History of Medicine by 105,286 contributed authors in WoS. We identified 157 countries that collaborated on scientific medical papers. By clustering 59,995 keywords, we were able to reveal the trend of History of Medicine research for the future. Our findings showed a positive association between traditional bibliometrics and social media metrics such as the Altmetric Attention Score in the History of Medicine literature (p < 0.05).
UNASSIGNED: Sharing research findings of articles in social scientific networks will increase the visibility of scientific works in History of Medicine research, which is one of the most important factors influencing the citation of articles. Additionally, our overview of the literature in the medical field allowed us to identify and examine gaps in the History of Medicine research.

BACKGROUND: Laboratory test overuse in hospitals is a form of healthcare waste that also harms patients. Developing and evaluating interventions to reduce this form of healthcare waste is critical. We detail the protocol for our study which aims to implement and evaluate the impact of an evidence-based, multicomponent intervention bundle on repetitive use of routine laboratory testing in hospitalized medical patients across adult hospitals in the province of British Columbia, Canada.
METHODS: We have designed a stepped-wedge cluster randomized trial to assess the impact of a multicomponent intervention bundle across 16 hospitals in the province of British Columbia in Canada. We will use the Knowledge to Action cycle to guide implementation and the RE-AIM framework to guide evaluation of the intervention bundle. The primary outcome will be the number of routine laboratory tests ordered per patient-day in the intervention versus control periods. Secondary outcome measures will assess implementation fidelity, number of all common laboratory tests used, impact on healthcare costs, and safety outcomes. The study will include patients admitted to adult medical wards (internal medicine or family medicine) and healthcare providers working in these wards within the participating hospitals. After a baseline period of 24 weeks, we will conduct a 16-week pilot at one hospital site. A new cluster (containing approximately 2-3 hospitals) will receive the intervention every 12 weeks. We will evaluate the sustainability of implementation at 24 weeks post implementation of the final cluster. Using intention to treat, we will use generalized linear mixed models for analysis to evaluate the impact of the intervention on outcomes.
CONCLUSIONS: The study builds upon a multicomponent intervention bundle that has previously demonstrated effectiveness. The elements of the intervention bundle are easily adaptable to other settings, facilitating future adoption in wider contexts. The study outputs are expected to have a positive impact as they will reduce usage of repetitive laboratory tests and provide empirically supported measures and tools for accomplishing this work.
BACKGROUND: This study was prospectively registered on April 8, 2024, via ClinicalTrials.gov Protocols Registration and Results System (NCT06359587). https://classic.
RESULTS: gov/ct2/show/NCT06359587?term=NCT06359587&recrs=ab&draw=2&rank=1.

BACKGROUND: A widely used approach for extracting information from gene expression data employs the construction of a gene co-expression network and the subsequent computational detection of gene clusters, called modules. WGCNA and related methods are the de facto standard for module detection. The purpose of this work is to investigate the applicability of more sophisticated algorithms toward the design of an alternative method with enhanced potential for extracting biologically meaningful modules.
RESULTS: We present self-learning gene clustering pipeline (SGCP), a spectral method for detecting modules in gene co-expression networks. SGCP incorporates multiple features that differentiate it from previous work, including a novel step that leverages gene ontology (GO) information in a self-leaning step. Compared with widely used existing frameworks on 12 real gene expression datasets, we show that SGCP yields modules with higher GO enrichment. Moreover, SGCP assigns highest statistical importance to GO terms that are mostly different from those reported by the baselines.
CONCLUSIONS: Existing frameworks for discovering clusters of genes in gene co-expression networks are based on relatively simple algorithmic components. SGCP relies on newer algorithmic techniques that enable the computation of highly enriched modules with distinctive characteristics, thus contributing a novel alternative tool for gene co-expression analysis.

Diabetic retinopathy is one of the most common microangiopathy in diabetes, essentially caused by abnormal blood glucose metabolism resulting from insufficient insulin secretion or reduced insulin activity. Epidemiological survey results show that about one third of diabetes patients have signs of diabetic retinopathy, and another third may suffer from serious retinopathy that threatens vision. However, the pathogenesis of diabetic retinopathy is still unclear, and there is no systematic method to detect the onset of the disease and effectively predict its occurrence. In this study, we used medical detection data from diabetic retinopathy patients to determine key biomarkers that induce disease onset through back propagation neural network algorithm and hierarchical clustering analysis, ultimately obtaining early warning signals of the disease. The key markers that induce diabetic retinopathy have been detected, which can also be used to explore the induction mechanism of disease occurrence and deliver strong warning signal before disease occurrence. We found that multiple clinical indicators that form key markers, such as glycated hemoglobin, serum uric acid, alanine aminotransferase are closely related to the occurrence of the disease. They respectively induced disease from the aspects of the individual lipid metabolism, cell oxidation reduction, bone metabolism and bone resorption and cell function of blood coagulation. The key markers that induce diabetic retinopathy complications do not act independently, but form a complete module to coordinate and work together before the onset of the disease, and transmit a strong warning signal. The key markers detected by this algorithm are more sensitive and effective in the early warning of disease. Hence, a new method related to key markers is proposed for the study of diabetic microvascular lesions. In clinical prediction and diagnosis, doctors can use key markers to give early warning of individual diseases and make early intervention.