Shiga toxin (Stx), the main virulence factor of Shiga-toxin-producing E. coli (STEC), was first discovered in Shigella dysenteriae strains. While several other bacterial species have since been reported to produce Stx, STEC poses the most significant risk to human health due to its widespread prevalence across various animal hosts that have close contact with human populations. Based on its biochemical and molecular characteristics, Shiga toxin can be grouped into two types, Stx1 and Stx2, among which a variety of variants and subtypes have been identified in various bacteria and host species. Interestingly, the different Stx subtypes appear to vary in their host distribution characteristics and in the severity of diseases that they are associated with. As such, this review provides a comprehensive overview on the bacterial species that have been recorded to possess stx genes to date, with a specific focus on the various Stx subtype variants discovered in STEC, their prevalence in certain host species, and their disease-related characteristics. This review provides a better understanding of the Stx subtypes and highlights the need for rapid and accurate approaches to toxin subtyping for the proper evaluation of the health risks associated with Shiga-toxin-related bacterial food contamination and human infections.

Neurodegenerative diseases are associated with heterogeneity in genetics, pathology, and clinical manifestation. Understanding this heterogeneity is particularly relevant for clinical prognosis and stratifying patients for disease modifying treatments. Recently, data-driven methods based on neuroimaging have been applied to investigate the subtyping of neurodegenerative disease, helping to disentangle this heterogeneity. We reviewed brain-based subtyping studies in aging and representative neurodegenerative diseases, including Alzheimer\'s disease, mild cognitive impairment, frontotemporal dementia, and Lewy body dementia, from January 2000 to November 2022. We summarized clustering methods, validation, robustness, reproducibility, and clinical relevance of 71 eligible studies in the present study. We found vast variations in approaches between studies, including ten neuroimaging modalities, 24 cluster algorithms, and 41 methods of cluster number determination. The clinical relevance of subtyping studies was evaluated by summarizing the analysis method of clinical measurements, showing a relatively low clinical utility in the current studies. Finally, we conclude that future studies of heterogeneity in neurodegenerative disease should focus on validation, comparison between subtyping approaches, and prioritise clinical utility.

UNASSIGNED: The overall survival of peripheral T-cell lymphoma (PTCL) is dismal. Histone deacetylase (HDAC) inhibitors have exhibited promising treatment outcomes for PTCL patients. Therefore, this work aims to systematically evaluate the treatment outcome and safety profile of HDAC inhibitor-based treatment for untreated and relapsed/refractory (R/R) PTCL patients.
UNASSIGNED: The prospective clinical trials of HDAC inhibitors for the treatment of PTCL were searched on the Web of Science, PubMed, Embase, ClinicalTrials.gov, and Cochrane Library database. The pooled overall response rate, complete response (CR) rate, and partial response rate were measured. The risk of adverse events was evaluated. Moreover, the subgroup analysis was utilized to assess the efficacy among different HDAC inhibitors and efficacy in different PTCL subtypes.
UNASSIGNED: For untreated PTCL, 502 patients in seven studies were involved, and the pooled CR rate was 44% (95% CI, 39-48%). For R/R PTCL patients, there were 16 studies included, and the CR rate was 14% (95% CI, 11-16%). The HDAC inhibitor-based combination therapy exhibited better efficacy when compared with HDAC inhibitor monotherapy for R/R PTCL patients (P = 0.02). In addition, the pooled CR rate was 17% (95% CI, 13-22%), 10% (95% CI, 5-15%), and 10% (95% CI, 5-15%) in the romidepsin, belinostat, and chidamide monotherapy subgroups, respectively. In the R/R angioimmunoblastic T-cell lymphoma subgroup, the pooled ORR was 44% (95% CI, 35-53%), higher than other subtypes. A total of 18 studies were involved in the safety assessment of treatment-related adverse events. Thrombocytopenia and nausea were the most common hematological and non-hematological adverse events, respectively.
UNASSIGNED: This meta-analysis demonstrated that HDAC inhibitors were effective treatment options for untreated and R/R PTCL patients. The combination of HDAC inhibitor and chemotherapy exhibited superior efficacy to HDAC inhibitor monotherapy in the R/R PTCL setting. Additionally, HDAC inhibitor-based therapy had higher efficacy in angioimmunoblastic T-cell lymphoma patients than that in other subtypes.

UNASSIGNED: Basal cell carcinoma (BCC) is the most common skin cancer, especially in fair-skinned adults, and the lifetime risk of its incidence in this population is estimated to be approximately 30%. We present a meta-analysis and systematic review summarizing the size of the growth rate in BCC, depending also on its subtypes.
UNASSIGNED: To find all relevant studies, online medical databases such as PubMed, Scopus, Embase, Web of Science, and Google Scholar were searched regarding the growth rate of BCC.
UNASSIGNED: A total of 7 studies were included in this review. Five studies contained data on the growth rate of basal cell carcinomas. The mean growth rate for the longer axis of the BCC was established to be 0.71 mm/month (standard error: 0.22).
UNASSIGNED: The presented analysis shows that BCC is generally a slow-growing tumour, with a mean growth rate of about 0.7 mm/month. However, it was proven that this growth rate differs depending on the BCC subtype.

Meningiomas are the most common intracranial tumors in adult patients. Although the majority of meningiomas are diagnosed as benign, approximately 20% of cases are high-grade tumors that require significant clinical treatment. The gold standard for grading central nervous system tumors comes from the World Health Organization Classification of Tumors of the central nervous system. Treatment options also depend on the location, imaging, and histopathological features of the tumor. This review will cover diagnostic strategies for meningiomas, including 2021 updates to the World Health Organization\'s grading of meningiomas. Meningioma treatment plans are variable and highly dependent on tumor grading. This review will also update the reader on developments in the treatment of meningiomas, including surgery, radiation therapy and monoclonal antibody treatment.

UNASSIGNED: This systematic review and meta-analysis evaluated the subtyped Blastocystis sp. isolated from humans in Iran.
UNASSIGNED: Blastocystis sp. is an anaerobic intestinal protozoan that infects humans as well as domestic and wild animals, i.e. mammals, amphibians, reptiles, and arthropods.
UNASSIGNED: A comprehensive search for papers published before April 2022 was undertaken utilizing English and Persian databases. The following MeSH keywords were used in the electronic search: (Blastocystis sp.) AND (molecular OR subtype) AND (prevalence OR epidemiology) AND Iran. The quality of the included studies was evaluated. Thereafter, a random-effects meta-analysis was conducted to estimate the pooled prevalence and odds ratios regarding the included studies.
UNASSIGNED: A total of 32 studies comprised of five case-control studies and 27 cross-sectional studies met the eligibility criteria. The overall pooled prevalence of subtyped Blastocystis sp. in Iran was estimated to be 10% (95% confidence interval: 6 to 15%). Eight subtypes of Blastocystis sp. (ST1- ST7 and ST9) were identified in our study, of which ST3 was the most common subtype (0.04); 0.02-0.07). The difference in subtypes between two case and control groups in reported studies was not significant, but the odds ratio of infection by ST3 (0.98; 95% CI, 0.30 to 3.20) was higher in cases.
UNASSIGNED: The current systematic review showed that with the exception of ST8 and ST12, all human Blastocystis sp. subtypes reported in the world are found in different parts of Iran.

UNASSIGNED: Heart failure is currently divided into three main forms, HFrEF, HFpEF, and HFmrEF, but its etiology is diverse and highly heterogeneous. Many studies reported a variety of novel subgroups in heart failure patients, with unsupervised machine learning methods. The aim of this scoping review is to provide insights into how these techniques can diagnose and manage HF faster and better, thus providing direction for future research and facilitating its routine use in clinical practice.
UNASSIGNED: The review was performed following PRISMA-SCR guideline. We searched the PubMed database for eligible publications. Studies were included if they defined new subgroups in HF patients using clustering analysis methods, and excluded if they are (1) Reviews, commentary, or editorials, (2) Studies not about defining new sub-types, or (3) Studies not using unsupervised algorithms. All study screening and data extraction were conducted independently by two investigators and narrative integration of data extracted from included studies was performed.
UNASSIGNED: Of the 498 studies identified, 47 were included in the analysis. Most studies (61.7%) were published in 2020 and later. The largest number of studies (46.8%) coming from the United States, and most of the studies were authored and included in the same country. The most commonly used machine learning method was hierarchical cluster analysis (46.8%), the most commonly used cluster variable type was comorbidity (61.7%), and the least used cluster variable type was genomics (12.8%). Most of the studies used data sets of less than 500 patients (48.9%), and the sample size had negative correlation with the number of clustering variables. The majority of studies (85.1%) assessed the association between cluster grouping and at least one outcomes, with death and hospitalization being the most commonly used outcome measures.
UNASSIGNED: This scoping review provides an overview of recent studies proposing novel HF subgroups based on clustering analysis. Differences were found in study design, study population, clustering methods and variables, and outcomes of interests, and we provided insights into how these studies were conducted and identify the knowledge gaps to guide future research.

Breast cancer is the most common malignant tumor in females. While most carcinomas are categorized as invasive carcinoma, no special type (NST), a diverse group of tumors with distinct pathologic and clinical features is also recognized, ranging in incidence from relatively more common to rare. So-called \"special histologic type\" tumors display more than 90% of a specific, distinctive histologic pattern, while a spectrum of tumors more often encountered in the salivary gland may also arise in the breast. Metaplastic carcinomas can present diagnostic challenges. Some uncommon tumors harbor pathognomonic genetic alterations. This article provides an overview of the key diagnostic points and differential diagnoses for this group of disparate lesions, as well as the salient clinical characteristics of each entity.

Thyroglossal duct cyst (TDC) commonly occurs in the neck just below the hyoid bone. Uncommon sites of TDC have been documented, and of these, an intra-thyroid location is very rare. We report such a rare intra-thyroid TDC (ITTDC) initially identified by ultrasound examination as an incidental thyroid imaging reporting and data system (TI-RADS) three lesion in the left thyroid lobe of a 59-year-old male patient with primary hyperparathyroidism due to a parathyroid adenoma. The preoperative ultrasound-guided fine-needle aspiration biopsy (US-FNAB) cytology of the thyroid lesion was interpreted as Bethesda III (atypia of undetermined significance or follicular lesion of undetermined significance). A left hemithyroidectomy and left superior parathyroidectomy were performed. The postoperative histology revealed the thyroid lesion to be an ITTDC. An incidental papillary thyroid microcarcinoma was also histologically revealed. The 2.5-year postoperative follow-up was uneventful. Based on literature searches, the clinical features, fine-needle aspiration biopsy (FNAB) cytology, histology, differential diagnosis, treatment, and follow-up of ITTDC were reviewed and discussed. A proposal to categorize ITTDC into two anatomical location subtypes is made. The liability of ITTDC to be misinterpreted on FNAB cytology due to rarity and lack of morphological specificity is emphasized.

Machine learning (ML) is increasingly used in research for subtype definition and risk prediction, particularly in cardiovascular diseases. No existing ML models are routinely used for cardiovascular disease management, and their phase of clinical utility is unknown, partly due to a lack of clear criteria. We evaluated ML for subtype definition and risk prediction in heart failure (HF), acute coronary syndromes (ACS) and atrial fibrillation (AF).
For ML studies of subtype definition and risk prediction, we conducted a systematic review in HF, ACS and AF, using PubMed, MEDLINE and Web of Science from January 2000 until December 2019. By adapting published criteria for diagnostic and prognostic studies, we developed a seven-domain, ML-specific checklist.
Of 5918 studies identified, 97 were included. Across studies for subtype definition (n = 40) and risk prediction (n = 57), there was variation in data source, population size (median 606 and median 6769), clinical setting (outpatient, inpatient, different departments), number of covariates (median 19 and median 48) and ML methods. All studies were single disease, most were North American (n = 61/97) and only 14 studies combined definition and risk prediction. Subtype definition and risk prediction studies respectively had limitations in development (e.g. 15.0% and 78.9% of studies related to patient benefit; 15.0% and 15.8% had low patient selection bias), validation (12.5% and 5.3% externally validated) and impact (32.5% and 91.2% improved outcome prediction; no effectiveness or cost-effectiveness evaluations).
Studies of ML in HF, ACS and AF are limited by number and type of included covariates, ML methods, population size, country, clinical setting and focus on single diseases, not overlap or multimorbidity. Clinical utility and implementation rely on improvements in development, validation and impact, facilitated by simple checklists. We provide clear steps prior to safe implementation of machine learning in clinical practice for cardiovascular diseases and other disease areas.