At the end of 2022 into early 2023, the UK Health Security Agency reported unusually high levels of scarlet fever and invasive disease caused by Streptococcus pyogenes (StrepA or group A Streptococcus). During this time, we collected and genome-sequenced 341 non-invasive throat and skin S. pyogenes isolates identified during routine clinical diagnostic testing in Sheffield, a large UK city. We compared the data with that obtained from a similar collection of 165 isolates from 2016 to 2017. Numbers of throat-associated isolates collected peaked in early December 2022, reflecting the national scarlet fever upsurge, while skin infections peaked later in December. The most common emm-types in 2022-2023 were emm1 (28.7 %), emm12 (24.9 %) and emm22 (7.7 %) in throat and emm1 (22 %), emm12 (10 %), emm76 (18 %) and emm49 (7 %) in skin. While all emm1 isolates were the M1UK lineage, the comparison with 2016-2017 revealed diverse lineages in other emm-types, including emm12, and emergent lineages within other types including a new acapsular emm75 lineage, demonstrating that the upsurge was not completely driven by a single genotype. The analysis of the capsule locus predicted that only 51 % of throat isolates would produce capsule compared with 78% of skin isolates. Ninety per cent of throat isolates were also predicted to have high NADase and streptolysin O (SLO) expression, based on the promoter sequence, compared with only 56% of skin isolates. Our study has highlighted the value in analysis of non-invasive isolates to characterize tissue tropisms, as well as changing strain diversity and emerging genomic features which may have implications for spillover into invasive disease and future S. pyogenes upsurges.

Human retinal organoids (ROs) have emerged as valuable tools for studying retinal development, modeling human retinal diseases, and screening drugs. However, their application is limited primarily due to time-intensive generation, high costs, and low reproducibility. Quality assessment of RO differentiation is crucial for their application in research. However, traditional methods such as morphological evaluation and immunohistochemical analysis have limitations due to their lack of precision and invasiveness, respectively. This study aims to identify non-invasive biomarkers for RO differentiation quality using exosomal microRNAs (miRNAs), which are known to reflect cell-specific functions and development in the retina. We differentiated ROs from human induced pluripotent stem cells (hiPSCs) and classified them into \'superior\' and \'inferior\' groups based on morphological and immunohistochemical criteria. Exosomes from the conditioned media were isolated and analyzed for miRNA content. Our findings revealed distinct miRNA profiles between superior and inferior ROs, with superior ROs exhibiting higher miRNA diversity and specifically up- or down-regulated miRNAs. Gene ontology and pathway enrichment analyses indicated that the target genes of these miRNAs are involved in neuron proliferation and differentiation. The study suggests the potential of exosomal hsa-miR-654-3p and hsa-miR-451a as non-invasive biomarkers for real-time monitoring of RO quality, facilitating the development of standardized, efficient, and cost-effective culture methods.

Barometric whole-body plethysmography (BWBP) is considered to be a particularly gentle method of assessing lung function in cats. However, there have been no studies to date investigating the stress experienced by cats during measurements. The prospective study included 48 healthy adult cats. Each cat was measured in the plethysmographic chamber for a total of 30 min and stress levels were determined every 10 min using a stress ethogram. At the beginning of measurements, 75% of cats were assessed as tense. Over the three time periods, a significant (p < 0.001) reduction in the total stress score was observed. In addition, all measurement parameters correlated significantly with the stress score, with the exception of enhanced pause and tidal volume. It can therefore be assumed that cats will initially experience stress during examination in the plethysmographic chamber, but stress will decrease significantly over time. As the stress level correlates with many measurement parameters, this should be taken into account when interpreting the results.

Colorectal cancer (CRC) is a significant global health challenge, ranking among the leading causes of cancer-related mortality worldwide. Despite efforts in prevention and early detection, CRC incidence and mortality rates are expected to rise substantially. Traditional screening methods like gFOBT, FIT, flexible sigmoidoscopy, colonoscopy, CTC, and colon capsule have limitations, including false positives/negatives, limited scope, or invasiveness. Recent developments in CRC screening involve DNA methylation biomarkers, showing promise in detecting early-stage CRC and precancerous lesions. Stool-based DNA testing is emerging as a noninvasive and convenient method for detecting CRC-associated DNA methylation alterations, offering potential for earlier detection compared to traditional methods. Several commercial stool-based DNA methylation tests targeting different genes associated with CRC have demonstrated varying sensitivity and specificity, some surpassing traditional screening methods. Challenges remain in optimizing their performance and accessibility. This review discusses how DNA methylation biomarkers could enhance CRC screening, and stool-based DNA methylation tests could revolutionize CRC screening practices, comparing them to the gold standard.

Prostate-Specific Antigen (PSA) based screening of prostate cancer (PCa) needs refinement. The aim of this study was the identification of urinary biomarkers to predict the Prostate Imaging-Reporting and Data System (PI-RADS) score and the presence of PCa prior to prostate biopsy. Urine samples from patients with elevated PSA were collected prior to prostate biopsy (cohort = 99). The re-analysis of mass spectrometry data from 45 samples was performed to identify urinary biomarkers to predict the PI-RADS score and the presence of PCa. The most promising candidates, i.e. SPARC-like protein 1 (SPARCL1), Lymphatic vessel endothelial hyaluronan receptor 1 (LYVE1), Alpha-1-microglobulin/bikunin precursor (AMBP), keratin 13 (KRT13), cluster of differentiation 99 (CD99) and hornerin (HRNR), were quantified by ELISA and validated in an independent cohort of 54 samples. Various biomarker combinations showed the ability to predict the PI-RADS score (AUC = 0.79). In combination with the PI-RADS score, the biomarkers improve the detection of prostate carcinoma-free men (AUC = 0.89) and of those with clinically significant PCa (AUC = 0.93). We have uncovered the potential of urinary biomarkers for a test that allows a more stringent prioritization of mpMRI use and improves the decision criteria for prostate biopsy, minimizing patient burden by decreasing the number of unnecessary prostate biopsies.

Protein aggregation is challenging for biopharmaceutical drug, because it affects the stability of protein formulations in real-time. However, current techniques for protein aggregate indication meet a number of limitations including limited aggregate size range, complex pre-treatments and lack of chromatographic approaches. Herein, a rapid, automatic, non-invasive and wide-scale coverage technique for aggregates indication is developed to overcome these challenges. Firstly, the response of low-field nuclear magnetic resonance (LF-NMR) to the aggregates is explored by making a comparison with certain established techniques. LF-NMR achieves a high sensitivity of water proton transverse relaxation rate (R2 of H2O, hereinafter referred as R2(H2O)) to protein aggregates from nanometer to micrometer. Then, the quantitative relationship between R2(H2O) and aggregates is investigated furtherly. R2(H2O) could serve as an all-size coverage protein aggregates indicator during development. As a non-invasive method, LF-NMR does not need any sample handling. It takes only 44 s for one test, and saves a lot of manpower, materials and costs. Compared with other established analytical techniques, the technique developed here could be a powerful tool for a rapid, automatic, non-invasive and wide-scale coverage technique for aggregates indication in biomacromolecule development.

Aim. This study aimed to investigate the correlation between seismocardiographic and echocardiographic systolic variables and whether a decrease in preload could be detected by the seismocardiography (SCG).Methods. This study included a total of 34 subjects. SCG and electrocardiography were recorded simultaneously followed by echocardiography (echo) in both supine and 30◦head-up tilted position. The SCG signals was segmented into individual heartbeats and systolic fiducial points were defined using a detection algorithm. Statistical analysis included correlation coefficient calculations and paired sample tests.Results. SCG was able to measure a decrease in preload by almost all of the examined systolic SCG variables. It was possible to correlate certain echo variables to SCG time intervals, amplitudes, and peak to peak intervals. Also, changes between supineand tilted position of some SCG variables were possible to correlate to changes in echo variables. LVET, IVCT, S\', strain, SR, SV, and LVEF were significantly correlated to relevant SCG variables.Conclusion. This study showed a moderate correlation, between systolic echo and systolic SCG variables. Additionally, systolic SCG variables were able to detect a decrease in preload.

OBJECTIVE: Non-invasive variceal risk stratification systems have not been validated in patients with hepatocellular carcinoma (HCC), which presents logistical barriers for patients in the setting of systemic HCC therapy. We aimed to develop and validate a non-invasive algorithm for the prediction of varices in patients with unresectable HCC.
METHODS: We performed a retrospective cohort study in 21 centers in the US including adult patients with unresectable HCC and Child Pugh A5-B7 cirrhosis diagnosed between 2007 and2019. We included patients who completed an esophagogastroduodonoscopy (EGD) within 12 months of index imaging but prior to HCC treatment. We divided the cohort into a 70:30 training set and validation set, with the goal of maximizing negative predictive value (NPV) to avoid EGD in low-risk patients.
RESULTS: We included 707 patients (median age 64.6 years, 80.6% male and 74.0% White). Median time from HCC diagnosis to EGD was 47 (IQR: 114) days, with 25.0% of patients having high-risk varices. A model using clinical variables alone achieved a NPV of 86.3% in the validation cohort, while a model integrating clinical and imaging variables had an NPV 97.4% in validation. The clinical and imaging model would avoid EGDs in over half of low-risk patients while misclassifying 7.7% of high-risk patients.
CONCLUSIONS: A model incorporating clinical and imaging data can accurately predict the absence of high-risk varices in patients with HCC and avoid EGD in many low-risk patients prior to the initiation of systemic therapy, thus expediting their care and avoiding treatment delays.

BACKGROUND: Differentiation of Non-cirrhotic Portal Fibrosis (NCPF) from chronic liver disease (CLD) in children and adolescents with portal hypertension (PHT) is challenging especially in cases where liver stiffness measurement (LSM) and hepatic venous pressure gradient are higher. This objective of the current study was to evaluate the diagnostic accuracy of the splenic stiffness measurement (SSM)/LSM ratio in the diagnosis of NCPF.
METHODS: From January 2019 to December 2023, consecutive children and adolescents of 6 months to 18 years of age with PHT (CLD and NCPF) were prospectively enrolled. Transient elastography (TE) for SSM and LSM, upper gastrointestinal endoscopy (UGIE), liver biopsy/trans-jugular liver biopsy, abdominal imaging, and laboratory evaluation were done. The relationship of TE parameters for diagnosis of NCPF and CLD was evaluated. Receiver-operating characteristic (ROC) statistics were applied using R Studio-4.2.2 statistical software.
RESULTS: One hundred and forty seven with CLD and 27 patients with NCPF were evaluated. Median age was 10.0 (IQR 2.4-14.0) years; 68.4% were males. The AUROC of SSM/LSM ratio was better (0.992, 95%CI 0.982-1.0001) than LSM (0.945, 95%CI0.913-0.977) and SSM (0.626, 95%CI0.258-0.489) for the diagnosis of NCPF. SSM/LSM ratio cut-off of 3.67 predicted NCPF with an excellent sensitivity (100%), specificity (95.9%), and diagnostic accuracy (95.91%). The AUROC of SSM/LSM ratio was excellent and outperformed other TE parameters in the subgroups, i.e., LSM between 10 and 20 kPa (0.982, 95%CI 0.947-1.000), without clinically significant varices (CSV) (1.000, 95%CI 1.000-1.000) and with CSV (0.993, 95%CI 0.983-1.000). Diagnostic performance of SSM/LSM Ratio was better than LSM for discriminating NCPF from CLD using McNemar test (p = 0.01).
CONCLUSIONS: The SSM/LSM ratio is an excellent tool in differentiating NCPF from CLD.

Background  Anemia is a severe public health problem in India affecting more than half of the population. To reduce its burden on the population, the Government of India under the Anemia Mukt Bharat program has adopted a monitoring strategy for the diagnosis and treatment of anemia. Point-of-care testing (POCT) devices play a pivotal role in testing hemoglobin at a community level where sophisticated laboratory instruments are not available. The majority of the currently available POCT devices are invasive in nature which have their own limitations. A non-invasive method of hemoglobin estimation will address many of the limitations of an invasive POCT instrument, which will further improve people\'s acceptability for hemoglobin testing. The Non-Invasive Anemia Detection App (NiADA) (Monere AI Private Limited, Kolkata, West Bengal, India), a non-invasive POCT application, uses artificial intelligence (AI) to predict the hemoglobin level from lower eyelid images. This real-time, point-of-care, low-cost solution uses a custom computer vision deep-learning algorithm to determine blood hemoglobin value.  Method  The study validates an AI-based smartphone application NiADA against laboratory hemoglobin estimation and a widely used point-of-care hemoglobin estimation instrument (HemoCue Hb 301; HemoCue AB, Angelholm, Skane County, Sweden). The study was conducted in a tertiary care hospital in Eastern India and recruited a total of 556 participants. These included 58 pediatric patients, 51 pregnant women, 214 adult females, and 224 adult males. Statistical analysis was performed using Python (Python Software Foundation, Wilmington, Delaware, United States). A p-value of < 0.05 was taken to be significant.  Result  The mean difference observed between NiADA and laboratory-estimated hemoglobin values came out to be -0.29 g/dL and -0.89 g/dL for adult females and males respectively, and 0.61 g/dL for pregnant women and -0.69g/dL for the pediatric population. The limits of agreement for NiADA were narrow at 2.77 to -2.18 g/dL for adult females, 3.76 to -1.96 g/dL for adult males, 1.89 to -3.29 g/dL for pregnant women, and 3.28 to -2.08 g/dL for the pediatric population. The sensitivity and specificity of the NiADA application against the laboratory estimation method were 75.8% and 53.8% for adult females, 70.0% and 48.3% for adult males, 23.8% and 90% for pregnant females, and 75% and 57% for the pediatric population.  Conclusion  As a non-invasive application, NiADA\'s performance is satisfactory and comparable with minimally invasive tools like HemoCue Hb 301 and other POCT devices.