An Advanced Machine Learning Model for a Web-Based Artificial Intelligence-Based Clinical Decision Support System Application: Model Development and Validation Study.

BACKGROUND: Breast cancer is a leading global health concern, necessitating advancements in recurrence prediction and management. The development of an artificial intelligence (AI)-based clinical decision support system (AI-CDSS) using ChatGPT addresses this need with the aim of enhancing both prediction accuracy and user accessibility. OBJECTIVE: This study aims to develop and validate an advanced machine learning model for a web-based AI-CDSS application, leveraging the question-and-answer guidance capabilities of ChatGPT to enhance data preprocessing and model development, thereby improving the prediction of breast cancer recurrence. METHODS: This study focused on developing an advanced machine learning model by leveraging data from the Tri-Service General Hospital breast cancer registry of 3577 patients (2004-2016). As a tertiary medical center, it accepts referrals from four branches-3 branches in the northern region and 1 branch on an offshore island in our country-that manage chronic diseases but refer complex surgical cases, including breast cancer, to the main center, enriching our study population's diversity. Model training used patient data from 2004 to 2012, with subsequent validation using data from 2013 to 2016, ensuring comprehensive assessment and robustness of our predictive models. ChatGPT is integral to preprocessing and model development, aiding in hormone receptor categorization, age binning, and one-hot encoding. Techniques such as the synthetic minority oversampling technique address the imbalance of data sets. Various algorithms, including light gradient-boosting machine, gradient boosting, and extreme gradient boosting, were used, and their performance was evaluated using metrics such as the area under the curve, accuracy, sensitivity, and F1-score. RESULTS: The light gradient-boosting machine model demonstrated superior performance, with an area under the curve of 0.80, followed closely by the gradient boosting and extreme gradient boosting models. The web interface of the AI-CDSS tool was effectively tested in clinical decision-making scenarios, proving its use in personalized treatment planning and patient involvement. CONCLUSIONS: The AI-CDSS tool, enhanced by ChatGPT, marks a significant advancement in breast cancer recurrence prediction, offering a more individualized and accessible approach for clinicians and patients. Although promising, further validation in diverse clinical settings is recommended to confirm its efficacy and expand its use.

Artificial intelligence-clinical decision support system for enhanced infectious disease management: Accelerating ceftazidime-avibactam resistance detection in Klebsiella pneumoniae.

BACKGROUND: Effective and rapid diagnostic strategies are required to manage antibiotic resistance in Klebsiella pneumonia (KP). This study aimed to design an artificial intelligence-clinical decision support system (AI-CDSS) using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) and machine learning for the rapid detection of ceftazidime-avibactam (CZA) resistance in KP to improve clinical decision-making processes. METHODS: Out of 107,721 bacterial samples, 675 specimens of KP with suspected multi-drug resistance were selected. These specimens were collected from a tertiary hospital and four secondary hospitals between 2022 and 2023 to evaluate CZA resistance. We used MALDI-TOF MS and machine learning to develop an AI-CDSS with enhanced speed of resistance detection. RESULTS: Machine learning models, especially light gradient boosting machines (LGBM), exhibited an area under the curve (AUC) of 0.95, indicating high accuracy. The predictive models formed the core of our newly developed AI-CDSS, enabling clinical decisions quicker than traditional methods using culture and antibiotic susceptibility testing by a day. CONCLUSIONS: The study confirms that MALDI-TOF MS, integrated with machine learning, can swiftly detect CZA resistance. Incorporating this insight into an AI-CDSS could transform clinical workflows, giving healthcare professionals immediate, crucial insights for shaping treatment plans. This approach promises to be a template for future anti-resistance strategies, emphasizing the vital importance of advanced diagnostics in enhancing public health outcomes.

Artificial Intelligence-Clinical Decision Support System in Infectious Disease Control: Combatting Multidrug-Resistant Klebsiella pneumoniae with Machine Learning.

Purpose: The World Health Organization has identified Klebsiella pneumoniae (KP) as a significant threat to global public health. The rising threat of carbapenem-resistant Klebsiella pneumoniae (CRKP) leads to prolonged hospital stays and higher medical costs, necessitating faster diagnostic methods. Traditional antibiotic susceptibility testing (AST) methods demand at least 4 days, requiring 3 days on average for culturing and isolating the bacteria and identifying the species using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS), plus an extra day for interpreting AST results. This lengthy process makes traditional methods too slow for urgent clinical situations requiring rapid decision-making, potentially hindering prompt treatment decisions, especially for fast-spreading infections such as those caused by CRKP. This research leverages a cutting-edge diagnostic method that utilizes an artificial intelligence-clinical decision support system (AI-CDSS). It incorporates machine learning algorithms for the swift and precise detection of carbapenem-resistant and colistin-resistant strains. Patients and Methods: We selected 4307 KP samples out of a total of 52,827 bacterial samples due to concerns about multi-drug resistance using MALDI-TOF MS and Vitek-2 systems for AST. It involved thorough data preprocessing, feature extraction, and machine learning model training fine-tuned with GridSearchCV and 5-fold cross-validation, resulting in high predictive accuracy, as demonstrated by the receiver operating characteristic and area under the curve (AUC) scores, laying the groundwork for our AI-CDSS. Results: MALDI-TOF MS analysis revealed distinct intensity profiles differentiating CRKP and susceptible strains, as well as colistin-resistant Klebsiella pneumoniae (CoRKP) and susceptible strains. The Random Forest Classifier demonstrated superior discriminatory power, with an AUC of 0.96 for detecting CRKP and 0.98 for detecting CoRKP. Conclusion: Integrating MALDI-TOF MS with machine learning in an AI-CDSS has greatly expedited the detection of KP resistance by approximately 1 day. This system offers timely guidance, potentially enhancing clinical decision-making and improving treatment outcomes for KP infections.

Recurrent Occupational Hantavirus Infections Linked to Feeder Rodent Breeding Farm, Taiwan, 2022.

We investigated 2 acute cases and 1 previous case of Seoul hantavirus infection in workers in a feeder rodent breeding farm in Taiwan. Prevalence of hantavirus IgG among the tested feeder rats was 37.5%. Appropriate prevention measures, including using disinfection protocols and personal protective equipment, are crucial to lowering risk.

Innovative strategies against superbugs: Developing an AI-CDSS for precise Stenotrophomonas maltophilia treatment.

OBJECTIVES: The World Health Organization named Stenotrophomonas maltophilia (SM) a critical multi-drug resistant threat, necessitating rapid diagnostic strategies. Traditional culturing methods require up to 96 h, including 72 h for bacterial growth, identification with matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) through protein profile analysis, and 24 h for antibiotic susceptibility testing. In this study, we aimed at developing an artificial intelligence-clinical decision support system (AI-CDSS) by integrating MALDI-TOF MS and machine learning to quickly identify levofloxacin and trimethoprim/sulfamethoxazole resistance in SM, optimizing treatment decisions. METHODS: We selected 8,662 SM from 165,299 MALDI-TOF MS-analysed bacterial specimens, collected from a major medical centre and four secondary hospitals. We exported mass-to-charge values and intensity spectral profiles from MALDI-TOF MS .mzML files to predict antibiotic susceptibility testing results, obtained with the VITEK-2 system using machine learning algorithms. We optimized the models with GridSearchCV and 5-fold cross-validation. RESULTS: We identified distinct spectral differences between resistant and susceptible SM strains, demonstrating crucial resistance features. The machine learning models, including random forest, light-gradient boosting machine, and XGBoost, exhibited high accuracy. We established an AI-CDSS to offer healthcare professionals swift, data-driven advice on antibiotic use. CONCLUSIONS: MALDI-TOF MS and machine learning integration into an AI-CDSS significantly improved rapid SM resistance detection. This system reduced the identification time of resistant strains from 24 h to minutes after MALDI-TOF MS identification, providing timely and data-driven guidance. Combining MALDI-TOF MS with machine learning could enhance clinical decision-making and improve SM infection treatment outcomes.

Monitoring coronavirus disease progression and clinical impact through quantitative viral load testing.

BACKGROUND: The viral load (VL) in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-infected individuals is critical for improving clinical treatment strategies, care, and decisions. Several studies have reported that the initial SARS-CoV-2 VL is associated with disease severity and mortality. Cycle threshold (Ct) values and/or copies/mL are often used to quantify VL. However, a multitude of platforms, primer/probe sets of different SARS-CoV-2 target genes, and reference material manufacturers may cause inconsistent interlaboratory interpretations. The first International Standard for SARS-CoV-2 RNA quantitative assays has allowed diagnostic laboratories to transition SARS-CoV-2 VL results into international units per milliliter (IU/mL). The Cobas SARS-CoV-2 Duo quantitative assay provides VL results expressed in IU/mL. MATERIALS AND METHODS: We enrolled 145 and 50 SARS-CoV-2-positive, hospitalized and 50-negative individuals at the Tri-Service General Hospital, Taiwan from January to May 2022. Each participant's electronic medical record was reviewed to determine asymptomatic, mild, moderate, and severe cases. Nasopharyngeal swabs were collected using universal transport medium. We investigated the association of SARS-CoV-2 VL with disease severity using the Cobas SARS-CoV-2 Duo quantitative assay and its functionality in clinical assessment and decision making to further improve clinical treatment strategies. Limit of detection (LOD) was assessed. RESULTS: All 50 SARS-CoV-2-negative samples confirmed negative for SARS-CoV-2, demonstrating 100 % specificity of the Cobas SARS-CoV-2 Duo assay. Patients with severe symptoms had longer hospital stays, and the length of hospital stay (30.56 days on average) positively correlated with the VL (8.22 ± 1.21 log10 IU/mL). Asymptomatic patients had the lowest VL (5.54 ± 2.06 log10 IU/mL) at admission and the shortest hospital stay (14.1 days on average). CONCLUSIONS: VL is associated with disease severity and duration of hospitalization; therefore, its quantification should be considered when making clinical care decisions and treatment strategies. The Cobas SARS-CoV-2 Duo assay provides a commutable unitage IU/mL for interlaboratory interpretations.

Host-Pathogen Interactions in K. pneumoniae Urinary Tract Infections: Investigating Genetic Risk Factors in the Taiwanese Population.

BACKGROUND: Klebsiella pneumoniae (K. pneumoniae) urinary tract infections pose a significant challenge in Taiwan. The significance of this issue arises because of the growing concerns about the antibiotic resistance of K. pneumoniae. Therefore, this study aimed to uncover potential genomic risk factors in Taiwanese patients with K. pneumoniae urinary tract infections through genome-wide association studies (GWAS). METHODS: Genotyping data are obtained from participants with a history of urinary tract infections enrolled at the Tri-Service General Hospital as part of the Taiwan Precision Medicine Initiative (TPMI). A case-control study employing GWAS is designed to detect potential susceptibility single-nucleotide polymorphisms (SNPs) in patients with K. pneumoniae-related urinary tract infections. The associated genes are determined using a genome browser, and their expression profiles are validated via the GTEx database. The GO, Reactome, DisGeNET, and MalaCards databases are also consulted to determine further connections between biological functions, molecular pathways, and associated diseases between these genes. RESULTS: The results identified 11 genetic variants with higher odds ratios compared to controls. These variants are implicated in processes such as adhesion, protein depolymerization, Ca2+-activated potassium channels, SUMOylation, and protein ubiquitination, which could potentially influence the host immune response. CONCLUSIONS: This study implies that certain risk variants may be linked to K. pneumoniae infections by affecting diverse molecular functions that can potentially impact host immunity. Additional research and follow-up studies are necessary to elucidate the influence of these risk variants on infectious diseases and develop targeted interventions for mitigating the spread of K. pneumoniae urinary tract infections.

DPPIV+ fibro-adipogenic progenitors form the niche of adult skeletal muscle self-renewing resident macrophages.

Adult tissue-resident macrophages (RMs) are either maintained by blood monocytes or through self-renewal. While the presence of a nurturing niche is likely crucial to support the survival and function of self-renewing RMs, evidence regarding its nature is limited. Here, we identify fibro-adipogenic progenitors (FAPs) as the main source of colony-stimulating factor 1 (CSF1) in resting skeletal muscle. Using parabiosis in combination with FAP-deficient transgenic mice (PdgfrαCreERT2 × DTA) or mice lacking FAP-derived CSF1 (PdgfrαCreERT2 × Csf1flox/null), we show that local CSF1 from FAPs is required for the survival of both TIM4- monocyte-derived and TIM4+ self-renewing RMs in adult skeletal muscle. The spatial distribution and number of TIM4+ RMs coincide with those of dipeptidyl peptidase IV (DPPIV)+ FAPs, suggesting their role as CSF1-producing niche cells for self-renewing RMs. This finding identifies opportunities to precisely manipulate the function of self-renewing RMs in situ to further unravel their role in health and disease.

Computed Tomography-Defined Sarcopenia in Outcomes of Patients with Unresectable Hepatocellular Carcinoma Undergoing Radioembolization: Assessment with Total Abdominal, Psoas, and Paraspinal Muscles.

Introduction: Sarcopenia is an adverse prognostic factor in patients with liver cirrhosis and hepatocellular carcinoma (HCC). Image-based sarcopenia assessment allows a standardized method to assess abdominal skeletal muscle. However, which is an index muscle for sarcopenia remains unclear. Therefore, we investigated whether sarcopenia defined according to different muscle groups with computed tomography (CT) scans can predict the prognosis of HCC after radioembolization. Methods: In this retrospective study, we analyzed patients who underwent radioembolization for unresectable HCC between January 2010 and December 2019. Before treatment, the total abdominal muscle (TAM), psoas muscle (PM), and paraspinal muscle (PS) areas were evaluated using a single CT slice at the third lumbar vertebra. In previous studies, sarcopenia was determined using the TAM, PM, and PS after stratifying by sex. Finally, we investigated each muscle-defined sarcopenia to decide whether or not it can serve as a prognostic factor for overall survival (OS). Results: We included 92 patients (74 men and 18 women). TAM, PM, and PS areas were significantly higher in the men than in the women (all p < 0.05). The patients with sarcopenia defined using PM, but not TAM and PS, exhibited significantly poorer OS than those without sarcopenia (median 15.3 vs. 23.8 months, p = 0.034, 0.821, and 0.341, respectively). After adjustment for clinical variables, such as body mass index, liver function, alpha-fetoprotein level, clinical staging, treatment response, and posttreatment curative therapy, PM-defined sarcopenia (hazard ratio: 1.899, 95% confidence interval: 1.087-3.315) remained an independent predictor for the poor OS. Conclusion: CT-assessed sarcopenia defined using PM was an independent prognostic factor for the poorer prognosis of unresectable HCC after radioembolization.

Clinical Evaluation of Direct Reverse Transcription PCR for Detection of SARS-CoV-2 Compared to Conventional RT-PCR in Patients with Positive Rapid Antigen Test Results during Circulation of Emerging Viral Variants.

The emergence of the Omicron (B.1.1.529) variant of SARS-CoV-2 has precipitated a new global wave of the COVID-19 pandemic. The rapid identification of SARS-CoV-2 infection is imperative for the effective mitigation of transmission. Diagnostic modalities such as rapid antigen testing and real-time reverse transcription polymerase chain reaction (RT-PCR) offer expedient turnaround times of 10-15 min and straightforward implementation. This preliminary study assessed the correlation between outcomes of commercially available rapid antigen tests for home use and conventional reverse transcription polymerase chain reaction (RT-PCR) assays using a limited set of clinical specimens. Patients aged 5-99 years presenting to the emergency department for SARS-CoV-2 testing were eligible for enrollment (n = 5652). Direct PCR and conventional RT-PCR were utilized for the detection of SARS-CoV-2. The entire cohort of 5652 clinical specimens was assessed by both modalities to determine the clinical utility of the direct RT-PCR assay. Timely confirmation of SARS-CoV-2 infection may attenuate viral propagation and guide therapeutic interventions. Additionally, direct RT-PCR as a secondary confirmatory test for at-home rapid antigen test results demonstrated sensitivity comparable to conventional RT-PCR, indicating utility for implementation in laboratories globally, especially in resource-limited settings with constraints on reagents, equipment, and skilled personnel. In summary, direct RT-PCR enables the detection of SARS-CoV-2 with a sensitivity approaching that of conventional RT-PCR while offering expedient throughput and shorter turnaround times. Moreover, direct RT-PCR provides an open-source option for diagnostic laboratories worldwide, particularly in low- and middle-income countries.

Spatiotemporal signaling underlies progressive vascular rarefaction in myocardial infarction.

Therapeutic angiogenesis represents a promising avenue to revascularize the ischemic heart. Its limited success is partly due to our poor understanding of the cardiac stroma, specifically mural cells, and their response to ischemic injury. Here, we combine single-cell and positional transcriptomics to assess the behavior of mural cells within the healing heart. In response to myocardial infarction, mural cells adopt an altered state closely associated with the infarct and retain a distinct lineage from fibroblasts. This response is concurrent with vascular rarefaction and reduced vascular coverage by mural cells. Positional transcriptomics reveals that the infarcted heart is governed by regional-dependent and temporally regulated programs. While the remote zone acts as an important source of pro-angiogenic signals, the infarct zone is accentuated by chronic activation of anti-angiogenic, pro-fibrotic, and inflammatory cues. Together, our work unveils the spatiotemporal programs underlying cardiac repair and establishes an association between vascular deterioration and mural cell dysfunction.

Accelerating pandemic response with the emergency Omicron RT-PCR test: A comprehensive solution for COVID-19 diagnosis and tracking.

The Omicron variant of concern (VOC) has surged in many countries and replaced the previously reported VOC. To identify different Omicron strains/sublineages on a rapid, convenient, and precise platform, we report a novel multiplex real-time reverse transcriptase polymerase chain reaction (RT-PCR) method in one tube based on the Omicron lineage sequence variants' information. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) subvariants were used in a PCR-based assay for rapid identification of Omicron sublineage genotyping in 1000 clinical samples. Several characteristic mutations were analyzed using specific primers and probes for the spike gene, del69-70, and F486V. To distinguish Omicron sublineages (BA.2, BA.4, and BA.5), the NSP1:141-143del in the ORF1a region and D3N mutation in membrane protein occurring outside the spike protein region were analyzed. Results from the real-time PCR assay for one-tube accuracy were compared to those of whole genome sequencing. The developed PCR assay was used to analyze 400 SARS-CoV-2 positive samples. Ten samples determined as BA.4 were positive for NSP1:141-143del, del69-70, and F486V mutations; 160 BA.5 samples were positive for D3N, del69-70, and F486V mutations, and 230 BA.2 samples were without del69-70. Screening these samples allowed the identification of epidemic trends at different time intervals. Our novel one-tube multiplex PCR assay was effective in identifying Omicron sublineages.

Activation of ß-catenin in mesenchymal progenitors leads to muscle mass loss.

Loss of muscle mass is a common manifestation of chronic disease. We find the canonical Wnt pathway to be activated in mesenchymal progenitors (MPs) from cancer-induced cachectic mouse muscle. Next, we induce ß-catenin transcriptional activity in murine MPs. As a result, we observe expansion of MPs in the absence of tissue damage, as well as rapid loss of muscle mass. Because MPs are present throughout the organism, we use spatially restricted CRE activation and show that the induction of tissue-resident MP activation is sufficient to induce muscle atrophy. We further identify increased expression of stromal NOGGIN and ACTIVIN-A as key drivers of atrophic processes in myofibers, and we verify their expression by MPs in cachectic muscle. Finally, we show that blocking ACTIVIN-A rescues the mass loss phenotype triggered by ß-catenin activation in MPs, confirming its key functional role and strengthening the rationale for targeting this pathway in chronic disease.

Comparison of thermal alkaline pretreatment and zinc acetate-catalyzed methanolysis (MtOH-ZnOAc) for anaerobic digestion of bioplastic waste.

The aim of this work was to study the effect of thermal alkaline pretreatment and zinc acetate-catalyzed methanolysis (MtOH-ZnOAc) in biogas production from bioplastic in anaerobic digestion. The pretreated bioplastic with MtOH-ZnOAc performs efficient solubilization and produced 205.7 ± 6.9 mL/g CODadded, which is higher than thermal alkaline degradation. The mesophilic condition produces more than 79% higher biogas compared with the thermophilic condition with the diluted pretreated bioplastic by 30 times. The kinetic study was well fit the experimental data and showed the correlation between cumulative biogas, production rate, and lag phase with mono- and two-stage system in batch fermentation. The two-stage system produced 315.6 ± 7.7 mL/g CODadded which was higher 67.2 ± 2.02 than the mono-stage system. Methanosaetaceae predominates among the Archaea, which are primarily responsible for methanogenesis, showing a contribution to a higher biogas production rate.

Spatial compartmentalization of signaling imparts source-specific functions on secreted factors.

Efficient regeneration requires multiple cell types acting in coordination. To better understand the intercellular networks involved and how they change when regeneration fails, we profile the transcriptome of hematopoietic, stromal, myogenic, and endothelial cells over 14 days following acute muscle damage. We generate a time-resolved computational model of interactions and identify VEGFA-driven endothelial engagement as a key differentiating feature in models of successful and failed regeneration. In addition, the analysis highlights that the majority of secreted signals, including VEGFA, are simultaneously produced by multiple cell types. To test whether the cellular source of a factor determines its function, we delete VEGFA from two cell types residing in close proximity: stromal and myogenic progenitors. By comparing responses to different types of damage, we find that myogenic and stromal VEGFA have distinct functions in regeneration. This suggests that spatial compartmentalization of signaling plays a key role in intercellular communication networks.

Monitoring algorithm of hospitalized patients in a medical center with SARS-CoV-2 (Omicron variant) infection: clinical epidemiological surveillance and immunological assessment.

Purpose: Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is a major healthcare threat worldwide. Since it was first identified in November 2021, the Omicron (B.1.1.529) variant of SARS-CoV-2 has evolved into several lineages, including BA.1, BA.2-BA.4, and BA.5. SARS-CoV-2 variants might increase transmissibility, pathogenicity, and resistance to vaccine-induced immunity. Thus, the epidemiological surveillance of circulating lineages using variant phenotyping is essential. The aim of the current study was to characterize the clinical outcome of Omicron BA.2 infections among hospitalized COVID-19 patients and to perform an immunological assessment of such cases against SARS-CoV-2. Patients and Methods: We evaluated the analytical and clinical performance of the BioIC SARS-CoV-2 immunoglobulin (Ig)M/IgG detection kit, which was used for detecting antibodies against SARS-CoV-2 in 257 patients infected with the Omicron variant. Results: Poor prognosis was noted in 38 patients, including eight deaths in patients characterized by comorbidities predisposing them to severe COVID-19. The variant-of-concern (VOC) typing and serological analysis identified time-dependent epidemic trends of BA.2 variants emerging in the outbreak of the fourth wave in Taiwan. Of the 257 specimens analyzed, 108 (42%) and 24 (9.3%) were positive for anti-N IgM and IgG respectively. Conclusion: The VOC typing of these samples allowed for the identification of epidemic trends by time intervals, including the B.1.1.529 variant replacing the B.1.617.2 variant. Moreover, antibody testing might serve as a complementary method for COVID-19 diagnosis. The combination of serological testing results with the reverse transcription-polymerase chain reaction cycle threshold value has potential value in disease prognosis, thereby aiding in epidemic investigations conducted by clinicians or the healthcare department.

The application of a novel 5-in-1 multiplex reverse transcriptase-polymerase chain reaction assay for rapid detection of SARS-CoV-2 and differentiation between variants of concern.

OBJECTIVES: We have established a novel 5-in-1 VOC assay to rapidly detect SARS-CoV-2 and immediately distinguish whether positive samples represent variants of concern (VOCs). METHODS: This assay could distinguish among five VOCs: Alpha, Beta, Gamma, Delta, and Omicron, in a single reaction tube. The five variants exhibit different single nucleotide polymorphisms (SNPs) in their viral genome, which can be used to distinguish them. We selected target SNPs in the spike gene, including N501Y, P681R, K417N, and deletion H69/V70 for the assay. RESULTS: The limit of detection of each gene locus was 80 copies per polymerase chain reaction. We observed a high consistency among the results when comparing the performance of our 5-in-1 VOC assay, whole gene sequencing, and the Roche VirSNiP SARS-CoV-2 test in retrospectively analyzing 150 clinical SARS-CoV-2 variant positive samples. The 5-in-1 VOC assay offers an alternative and rapid high-throughput test for most diagnostic laboratories in a flexible sample-to-result platform. CONCLUSION: The assay can also be applied in a commercial platform with the completion of the SARS-CoV-2 confirmation test and identification of its variant within 2.5 hours.

TNFα and IFNγ cooperate for efficient pro- to anti-inflammatory transition of macrophages during muscle regeneration.

IFNγ is traditionally known as a proinflammatory cytokine with diverse roles in antimicrobial and antitumor immunity. Yet, findings regarding its sources and functions during the regeneration process following a sterile injury are conflicting. Here, we show that natural killer (NK) cells are the main source of IFNγ in regenerating muscle. Beyond this cell population, IFNγ production is limited to a small population of T cells. We further show that NK cells do not play a major role in muscle regeneration following an acute injury or in dystrophic mice. Surprisingly, the absence of IFNγ per se also has no effect on muscle regeneration following an acute injury. However, the role of IFNγ is partially unmasked when TNFα is also neutralized, suggesting a compensatory mechanism. Using transgenic mice, we showed that conditional inhibition of IFNGR1 signaling in muscle stem cells or fibro-adipogenic progenitors does not play a major role in muscle regeneration. In contrast to common belief, we found that IFNγ is not present in the early inflammatory phase of the regeneration process but rather peaks when macrophages are acquiring an anti-inflammatory phenotype. Further transcriptomic analysis suggests that IFNγ cooperates with TNFα to regulate the transition of macrophages from pro- to anti-inflammatory states. The absence of the cooperative effect of these cytokines on macrophages, however, does not result in significant regeneration impairment likely due to the presence of other compensatory mechanisms. Our findings support the arising view of IFNγ as a pleiotropic inflammatory regulator rather than an inducer of the inflammatory response.

Critical pediatric neurological illness associated with COVID-19 (Omicron BA.2.3.7 variant) infection in Taiwan: immunological assessment and viral genome analysis in tertiary medical center.

OBJECTIVES: Since April 2022, another wave of the Omicron epidemic has struck Taiwanese society, and children with severe neurological complications have been reported frequently. A few cases even developed acute fulminant encephalitis. To investigate the possible causes of the increased incidence of such complications in Taiwan, we reviewed several cases of pediatric patients with severe neurological symptoms. METHODS: We collected the medical records of pediatric patients with COVID-19 infection who presented with severe neurological symptoms. The COVID-19 infection was diagnosed by nasal swab reverse transcriptase-polymerase chain reaction. The remaining samples were sent for whole genome sequencing and spike (S) protein amino acid variation mapping. RESULTS: The increase of several inflammatory markers was observed in all patients included in this study. However, none of the cerebrospinal fluid samples tested positive for SARS-CoV-2. The result of whole genome sequencing showed that all the sequences belonged to the lineage BA.2.3.7. However, the sequences had a K97E mutation in the S protein that differed from other BA.2.3.7 lineage strains, which was located at the S protein N-terminal domain. CONCLUSION: The new mutation in the S protein, which had not previously been observed but was discovered in this study, potentially explains the sudden increase in incidence of extremely adverse neurological symptoms in pediatric patients.

Emergency SARS-CoV-2 variants of concern: rapidly direct RT-qPCR detection without RNA extraction, clinical comparison, cost-effective, and high-throughput.

Since the late 2020, the evolution of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants of concern has been characterized by the emergence of spike protein mutations, and these variants have become dominant worldwide. The gold standard SARS-CoV-2 diagnosis protocol requires two complex processes, namely, RNA extraction and real-time reverse transcriptase polymerase chain reaction (RT-PCR). There is a need for a faster, simpler, and more cost-effective detection strategy that can be utilized worldwide, especially in developing countries. We propose the novel use of direct RT-qPCR, which does not require RNA extraction or a preheating step. For the detection, retrospectively, we used 770 clinical nasopharyngeal swabs, including positive and negative samples. The samples were subjected to RT-qPCR in the N1 and E genes using two different thermocyclers. The limit of detection was 30 copies/reaction for N1 and 60 copies/reaction for E. Analytical sensitivity was assessed for the developed direct RT-qPCR; the sensitivity was 95.69%, negative predictive value was 99.9%, accuracy of 99.35%, and area under the curve was 0.978. This novel direct RT-qPCR diagnosis method without RNA extraction is a reliable and high-throughput alternative method that can significantly save cost, labor, and time during the coronavirus disease 2019 pandemic.