Traditional microbial diagnostic methods face many obstacles such as sample handling, culture difficulties, misidentification, and delays in determining susceptibility. The advent of artificial intelligence (AI) has markedly transformed microbial diagnostics with rapid and precise analyses. Nonetheless, ethical considerations accompany AI adoption, necessitating measures to uphold patient privacy, mitigate biases, and ensure data integrity. This review examines conventional diagnostic hurdles, stressing the significance of standardized procedures in sample processing. It underscores AI\'s significant impact, particularly through machine learning (ML), in microbial diagnostics. Recent progressions in AI, particularly ML methodologies, are explored, showcasing their influence on microbial categorization, comprehension of microorganism interactions, and augmentation of microscopy capabilities. This review furnishes a comprehensive evaluation of AI\'s utility in microbial diagnostics, addressing both advantages and challenges. A few case studies including SARS-CoV-2, malaria, and mycobacteria serve to illustrate AI\'s potential for swift and precise diagnosis. Utilization of convolutional neural networks (CNNs) in digital pathology, automated bacterial classification, and colony counting further underscores AI\'s versatility. Additionally, AI improves antimicrobial susceptibility assessment and contributes to disease surveillance, outbreak forecasting, and real-time monitoring. Despite a few limitations, integration of AI in diagnostic microbiology presents robust solutions, user-friendly algorithms, and comprehensive training, promising paradigm-shifting advancements in healthcare.

In recent years, infectious disease diagnosis has increasingly turned to host-centered approaches as a complement to pathogen-directed ones. The former, however, typically requires the interpretation of complex multiple biomarker datasets to arrive at an informative diagnostic outcome. This report describes a machine learning (ML)-based classification workflow that is intended as a template for researchers seeking to apply ML approaches for developing host-based infectious disease biomarker classifiers. As an example, we built a classification model that could accurately distinguish between three disease etiology classes: bacterial, viral, and normal in human sera using host protein biomarkers of known diagnostic utility. After collecting protein data from known disease samples, we trained a series of increasingly complex Auto-ML models until arriving at an optimized classifier that could differentiate viral, bacterial, and non-disease samples. Even when limited to a relatively small training set size, the model had robust diagnostic characteristics and performed well when faced with a blinded sample set. We present here a flexible approach for applying an Auto-ML-based workflow for the identification of host biomarker classifiers with diagnostic utility for infectious disease, and which can readily be adapted for multiple biomarker classes and disease states.

The loop-mediated isothermal amplification (LAMP) technique is a great alternative to PCR-based methods, as it is fast, easy to use and works with high sensitivity and specificity without the need for expensive instruments. However, one of the limitations of LAMP is difficulty in achieving the simultaneous detection of several targets in a single tube, as the methodologies that allow this rely on fluorogenic probes containing specific target sequences, complicating their adaptation and the optimization of assays. Here, we summarize different methods for the development of multiplex LAMP assays based on sequence-specific detection, illustrated with a schematic representation of the technique, and evaluate their practical application based on the real-time detection and quantification of results, the possibility to visualize the results at a glance, the prior stabilization of reaction components, promoting the point-of-care use, the maximum number of specific targets amplified, and the validation of the technique in clinical samples. The various LAMP multiplexing methodologies differ in their operating conditions and mechanism. Each methodology has its advantages and disadvantages, and the choice among them will depend on specific application interests.

BACKGROUND: The postoperative complication rate is 30-64% among patients undergoing muscle-invasive and recurrent high-risk non-muscle-invasive bladder cancer surgery. Preoperative risky alcohol use increases the risk. The aim was to evaluate the accuracy of markers for identifying preoperative risky alcohol.
METHODS: Diagnostic test sub-study of a randomized controlled trial (STOP-OP trial), based on a cohort of 94 patients scheduled for major bladder cancer surgery. Identification of risky alcohol use using Timeline Follow Back interviews (TLFB) were compared to the AUDIT-C questionnaire and three biomarkers: carbohydrate-deficient transferrin in plasma (P-CDT), phosphatidyl-ethanol in blood (B-PEth), and ethyl glucuronide in urine (U-EtG).
RESULTS: The correlation between TLFB and AUDIT-C was strong (ρ = 0.75), while it was moderate between TLFB and the biomarkers (ρ = 0.55-0.65). Overall, sensitivity ranged from 56 to 82% and specificity from 38 to 100%. B-PEth showed the lowest sensitivity at 56%, but the highest specificity of 100%. All tests had high positive predictive values (79-100%), but low negative predictive values (42-55%).
CONCLUSIONS: Despite high positive predictive values, negative predictive values were weak compared to TLFB. For now, TLFB interviews seem preferable for preoperative identification of risky alcohol use.

UNASSIGNED: As a common endocrine and metabolic disorder, polycystic ovary syndrome (PCOS) is mostly associated with an obese phenotype. The present research focuses on the clinical significance of miR-379 in obesity-PCOS and attempts to elucidate its potential mechanisms.
UNASSIGNED: Healthy individuals (n = 46), obesity-PCOS (n = 92), and non-obesity PCOS (n = 31) subjects were enrolled. Quantitative real-time polymerase chain reaction (qRT-PCR) was conducted to examine the level of serum miR-379. The receiver operating characteristic (ROC) curve and logistic regressions were applied to reveal the diagnostic significance. Dual luciferase reporters were performed to validate the targeting relationships. And cell count kit (CCK-8) assay was used to detect cell proliferation.
UNASSIGNED: Serum miR-379 was highly expressed in PCOS patients (P < 0.05), in especially obesity-PCOS patients. Higher miR-379 was associated with greater body mass index (BMI), higher bioavailable testosterone (bT), and greater insulin resistance (IR). Additionally, miR-379 was an independent risk factor for the development of obesity-PCOS. The sensitivity of miR-379 in identifying patients with obesity-PCOS from healthy or non-obesity-PCSO patients was 81.52% and 72.83%, and the specificity was 86.96% and 80.65%. Semaphorin 3 A (SEMA3A) was identified as a target of miR-379 and was reduced in the patients with obesity PCOS (P < 0.05). Inhibition of miR-375 reduced KGN proliferation, but this reduction was partially restored by silencing of SEMA3A (P < 0.05).
UNASSIGNED: Elevated miR-379 assists the diagnosis of obesity-PCOS and regulates the proliferation of KGN by targeting SEMA3A engaged in disease development.

Hepatocellular carcinoma (HCC) is the most common primary malignancy in the liver and is the third cause of cancer-related death worldwide. Surveillance with abdominal ultrasound should be offered to individuals at high risk for developing HCC. Accurate diagnosis, staging, and liver function are crucial when determining the optimal therapeutic approach. The BCLC staging system is widely endorsed in Western countries. Managing this pathology requires a multidisciplinary, personalized approach, generally with a multimodal strategy. Surgery remains the only curative option, albeit local and systemic therapy may also increase survival when surgery is not suitable. In advanced disease, systemic treatment should be offered to patients with ECOG/PS 0-1 and Child-Pugh class A.

Recent investigations have demonstrated abnormal expression of non-coding RNAs in pituitary adenomas. Cntribution of many lncRNAs to the pathogenesis of these tumors has not been evaluated yet. HOTTIP, ANRIL, PANDAR, PCGEM1 and HOTAIR are among lncRNAs with established roles in the pathogenesis of human cancers, particularly those originated from endocrine organs. The current study aims at assessment of expression of these lncRNAs in pituitary adenomas in comparison with the adjacent non-cancerous pituitary tissues. HOTAIR expression was absent from the majority of adenoma and non-tumoral samples. Expression of HOTTIP was significantly higher in non-functioning pituitary adenoma (NFPA) samples compared with paired normal samples (expression ratio (95 % CI)= 2.1 (1.13-2.1), P value=0.03). Expression of PANDAR was higher in total adenoma samples compared with paired normal samples (expression ratio (95 % CI)= 1.91 (1.16-3.13), P value=0.02). Expression of ANRIL was higher in NFPA samples compared with paired normal samples (expression ratio (95 % CI)= 1.94 (1.05-3.6), P value=0.048) and in total adenoma samples compared with paired normal samples (expression ratio (95 % CI)= 1.82 (1.11-2.98), P value=0.025). The current study raises the possibility of contribution of lncRNAs in the pathogenesis of at least some subtypes of pituitary adenomas and warrant further functional studies in this field.

RT-LAMP is an effective alternative to RT-PCR-based diagnostics, offering high specificity, sensitivity, and rapid results. One notable advantage is the robustness of its enzymes, allowing for direct amplification from crude samples without the need for prior isolation of RNA. Colorimetric LAMP is particularly attractive as it eliminates the need for complex instrumentation, making it suitable for point-of-care applications. Here, we present a comprehensive step-by-step protocol for establishing an RT-LAMP-based test for direct detection of SARS-CoV-2 genomic RNA in saliva samples using different colorimetric detection methods. Importantly, this versatile test can be easily adapted to detect emerging pathogens.

Grapevine enamovirus 1 (GEV1) belongs to the genus Enamovirus, in the family Solemoviridae. It has been reported from several countries infecting grapevines including Brazil (Silva et al. 2017), China (Ren et al. 2021) and France (Hily et al. 2022). To assess the prevalence and diversity of economically important grapevine viruses in nine Canadian vineyards, total RNA and double-stranded RNA (dsRNA) (Fall et al. 2020) were extracted from 30 and 100 composite samples respectively, with each consisting of five vines of the same cultivars. The cultivars included in this study are Frontenac noir (n=34), Vidal (n=32), Marquette (n=33), Riesling (n=31), and Pinot noir (n=31). The total RNA and dsRNA samples were subsequently multiplexed and diagnosed by high-throughput sequencing (HTS) on NovaSeq (600 S4 PE100) and MiSeq (2 × 250 cycle PE) respectively. From NovaSeq and MiSeq sequencing, an average of 410,000 to 1.3 million reads/sample were obtained, respectively, with mapped viral reads representing 10.92% to 12.48% of the total reads. After sequence quality was verified using Trimmomatic v.0.40 (Bolger et al. 2014), the clean sequences were screened against all possible viruses in the databases using the Virtool (Rott et al. 2017) and VirFind virus detection pipelines (Ho and Tzanetakis 2014). GEV1 was detected in clean sequences from two, three, and two leaf samples of cultivars \'Marquette\' \'Riesling\' and \'Frontenac noir\' respectively. Six of the seven HTS-assembled GEV1 genomes were partial, ranging from 4,523 to 6,000 nucleotide (nt) with genome coverage varying from 71% to 89%. Only one 6,314 nt long assembled contig (Accession No. OR021829), represented a nearly complete genome, being only 53 and 3 nt shorter than Sd-CG (MT536978) at 5\' and 3\' untranslated regions (UTR), respectively. Isolate 3- Riesling-CAN (OR021829) shares 90.56 to 94.19% nt identities with several GEV1isolates at 96-99% of query coverage. Phylogenetically, OR021829 is closer to GEV1 isolates from France and China (Figure S1). To validate the HTS results, the developed primer pair SetF and Set1R (Silva et al., 2017) was used for RT-PCR detection. The amplicons from all seven HTS-positive samples were sequenced using Sanger sequencing, confirming the presence of GEV-1 in three studied grape cultivars in Canadian vineyards. Symptoms associated with the specific GEV1-infected vines could not be explained as composite samples were used. Each of the combined samples HTS library also tested positive for at least one of the known grape virus/viroids, namely grapevine leafroll associated-virus -3, grapevine pinot gris virus, grapevine rupestris stem pitting-associated virus, Marafivirus syrahense grapevine Syrah virus-1 and hop stunt viroid. To our knowledge, this is the first report of GEV1 being detected in grapevines in Canada, or in any North American vineyard. GEV1 is a relatively new virus, and its biology remains largely unknown. Based on this sequence new GEV1 primers can be developed to know the genetic variability among GEV-1 and improve the detection of this virus in vineyards.

BACKGROUND: Dermoscopy is a growing field that uses microscopy to allow dermatologists and primary care physicians to identify skin lesions. For a given skin lesion, a wide variety of differential diagnoses exist, which may be challenging for inexperienced users to name and understand.
OBJECTIVE: In this study, we describe the creation of the dermoscopy differential diagnosis explorer (D3X), an ontology linking dermoscopic patterns to differential diagnoses.
METHODS: Existing ontologies that were incorporated into D3X include the elements of visuals ontology and dermoscopy elements of visuals ontology, which connect visual features to dermoscopic patterns. A list of differential diagnoses for each pattern was generated from the literature and in consultation with domain experts. Open-source images were incorporated from DermNet, Dermoscopedia, and open-access research papers.
RESULTS: D3X was encoded in the OWL 2 web ontology language and includes 3041 logical axioms, 1519 classes, 103 object properties, and 20 data properties. We compared D3X with publicly available ontologies in the dermatology domain using a semiotic theory-driven metric to measure the innate qualities of D3X with others. The results indicate that D3X is adequately comparable with other ontologies of the dermatology domain.
CONCLUSIONS: The D3X ontology is a resource that can link and integrate dermoscopic differential diagnoses and supplementary information with existing ontology-based resources. Future directions include developing a web application based on D3X for dermoscopy education and clinical practice.