Development of a Machine Learning Model for Predicting Weaning Outcomes Based Solely on Continuous Ventilator Parameters during Spontaneous Breathing Trials

Discontinuing mechanical ventilation remains challenging. We developed a machine learning model to predict weaning outcomes using only continuous monitoring parameters obtained from ventilators during spontaneous breathing trials (SBTs). Patients who received mechanical ventilation in the medical intensive care unit at a tertiary university hospital from 2019–2021 were included in this study. During the SBTs, three waveforms and 25 numerical data were collected as input variables. The proposed convolutional neural network (CNN)-based weaning prediction model extracts features from input data with diverse lengths. Among 138 enrolled patients, 35 (25.4%) experienced weaning failure. The dataset was randomly divided into training and test sets (8:2 ratio). The area under the receiver operating characteristic curve for weaning success by the prediction model was 0.912 (95% confidence interval [CI], 0.795–1.000), with an area under the precision-recall curve of 0.767 (95% CI, 0.434–0.983). Furthermore, we used gradient-weighted class activation mapping technology to provide visual explanations of the model’s prediction, highlighting influential features. This tool can assist medical staff by providing intuitive information regarding readiness for extubation without requiring any additional data collection other than SBT data. The proposed predictive model can assist clinicians in making ventilator weaning decisions in real time, thereby improving patient outcomes

Genome Analysis of Multi- and Extensively-Drug-Resistant Tuberculosis from KwaZulu-Natal, South Africa

The KZN strain family of Mycobacterium tuberculosis is a highly virulent strain endemic to the KwaZulu-Natal region of South Africa, which has recently experienced an outbreak of extensively-drug resistant tuberculosis. To investigate the causes and evolution of drug-resistance, we determined the DNA sequences of several clinical isolates - one drug-susceptible, one multi-drug resistant, and nine extensively drug-resistant - using whole-genome sequencing. Analysis of polymorphisms among the strains is consistent with the drug-susceptibility profiles, in that well-known mutations are observed that are correlated with resistance to isoniazid, rifampicin, kanamycin, ofloxacin, ethambutol, and pyrazinamide. However, the mutations responsible for rifampicin resistance in rpoB and pyrazinamide in pncA are in different nucleotide positions in the multi-drug-resistant and extensively drug-resistant strains, clearly showing that they acquired these mutations independently, and that the XDR strain could not have evolved directly from the MDR strain (though it could have arisen from another similar MDR strain). Sequencing of eight additional XDR strains from other areas of KwaZulu-Natal shows that they have identical drug resistant mutations to the first one sequenced, including the same polymorphisms at sites associated with drug resistance, supporting the theory that this represents a case of clonal expansion

Bortezomib/docetaxel combination therapy in patients with anthracycline-pretreated advanced/metastatic breast cancer: a phase I/II dose-escalation study

The aim of this study was to determine the dose-limiting toxicities (DLTs) and maximum tolerated dose (MTD) of bortezomib plus docetaxel in patients with anthracycline-pretreated advanced/metastatic breast cancer. Forty-eight patients received up to eight 21-day cycles of docetaxel (60–100 mg m−2 on day 1) plus bortezomib (1.0–1.5 mg m−2 on days 1, 4, 8, and 11). Pharmacodynamic and pharmacokinetic analyses were performed in a subset of patients. Five patients experienced DLTs: grade 3 bone pain (n=1) and febrile neutropenia (n=4). The MTD was bortezomib 1.5 mg m−2 plus docetaxel 75 mg m−2. All 48 patients were assessable for safety and efficacy. The most common adverse events were diarrhoea, nausea, alopecia, asthenia, and vomiting. The most common grade 3/4 toxicities were neutropenia (44%), and febrile neutropenia and diarrhoea (each 19%). Overall patient response rate was 29%. Median time to progression was 5.4 months. In patients with confirmed response, median time to response was 1.3 months and median duration of response was 3.2 months. At the MTD, response rate was 38%. Pharmacokinetic characteristics of bortezomib/docetaxel were comparable with single-agent data. Addition of docetaxel appeared not to affect bortezomib inhibition of 20S proteasome activity. Mean alpha-1 acid glycoprotein concentrations increased from baseline at nearly all time points across different bortezomib dose levels. Bortezomib plus docetaxel is an active combination for anthracycline-pretreated advanced/metastatic breast cancer. The safety profile is manageable and consistent with the side effects of the individual agents

Cancer Biomarker Discovery: The Entropic Hallmark

Background: It is a commonly accepted belief that cancer cells modify their transcriptional state during the progression of the disease. We propose that the progression of cancer cells towards malignant phenotypes can be efficiently tracked using high-throughput technologies that follow the gradual changes observed in the gene expression profiles by employing Shannon's mathematical theory of communication. Methods based on Information Theory can then quantify the divergence of cancer cells' transcriptional profiles from those of normally appearing cells of the originating tissues. The relevance of the proposed methods can be evaluated using microarray datasets available in the public domain but the method is in principle applicable to other high-throughput methods. Methodology/Principal Findings: Using melanoma and prostate cancer datasets we illustrate how it is possible to employ Shannon Entropy and the Jensen-Shannon divergence to trace the transcriptional changes progression of the disease. We establish how the variations of these two measures correlate with established biomarkers of cancer progression. The Information Theory measures allow us to identify novel biomarkers for both progressive and relatively more sudden transcriptional changes leading to malignant phenotypes. At the same time, the methodology was able to validate a large number of genes and processes that seem to be implicated in the progression of melanoma and prostate cancer. Conclusions/Significance: We thus present a quantitative guiding rule, a new unifying hallmark of cancer: the cancer cell's transcriptome changes lead to measurable observed transitions of Normalized Shannon Entropy values (as measured by high-throughput technologies). At the same time, tumor cells increment their divergence from the normal tissue profile increasing their disorder via creation of states that we might not directly measure. This unifying hallmark allows, via the the Jensen-Shannon divergence, to identify the arrow of time of the processes from the gene expression profiles, and helps to map the phenotypical and molecular hallmarks of specific cancer subtypes. The deep mathematical basis of the approach allows us to suggest that this principle is, hopefully, of general applicability for other diseases

Deep-learning-based smartphone application for self-diagnosis of scleral jaundice in patients with hepatobiliary and pancreatic diseases

Outpatient detection of total bilirubin levels should be performed regularly to monitor the recurrence of jaundice in hepatobiliary and pancreatic disease patients. However, frequent hospital visits for blood testing are burdensome for patients with poor medical conditions. This study validates a novel deep-learning-based smartphone application for the self-diagnosis of scleral jaundice in such patients. The system predicts total serum bilirubin levels using the deep-learning-based regression analysis of scleral photos taken by the smartphone’s built-in camera. Enrolled patients were randomly assigned to either the training cohort (n = 90, 1034 photos) or the valida-tion cohort (n = 40, 426 photos). The intraclass correlation coefficient value for predicted serum total bilirubin (PSB) derived from the images repeatedly taken at the same time for the same patient showed good reliability (0.86). A strong correlation between measured serum total bilirubin (MSB) and PSB was observed in the subgroup with MSB levels ≥1.5 mg/dL (Spearman rho = 0.70, p < 0.001). The receiver operating characteristic curve for PSB showed that the area under the curve was 0.93, demonstrating good test performance as a predictor of hyperbilirubinemia (p < 0.001). Using a cut-off PSB ≥1.5, the prediction sensitivity of hyperbilirubinemia was 80.0%, with a specificity of 92.6%. Hence, the tool is effective for patient monitoring

Change of iron content in brain regions after intravenous iron therapy in restless legs syndrome: quantitative susceptibility mapping study

Study Objectives: The pathomechanism of restless legs syndrome (RLS) is related to brain iron deficiency and iron therapy is effective for RLS; however, the effect of iron therapy on human brain iron state has never been studied with magnetic resonance imaging. This study aimed to investigate the change of brain iron concentrations in patients with RLS after intravenous iron therapy using quantitative susceptibility mapping (QSM). Methods: We enrolled 31 RLS patients and 20 healthy controls. All participants underwent initial baseline (t0) assessment using brain magnetic resonance imaging, serum iron status, and sleep questionnaires including international RLS Study Group rating scale (IRLS). RLS patients underwent follow-up tests at 6 and 24 weeks (t1 and t2) after receiving 1000 mg ferric carboxymaltose. Iron content of region-of-interest on QSM images was measured for 13 neural substrates using the fixed-shaped method. Results: RLS symptoms evaluated using IRLS were significantly improved after iron treatment (t0: 29.7 ± 6.5, t1: 19.5 ± 8.5, t2: 21.3 ± 10.1; p <. 001). There was no significant difference in susceptibility values between the controls and RLS patients at t0. In the caudate nucleus, putamen, and pulvinar thalamus of RLS patients, the QSM values differed significantly for three timepoints (p =. 035,. 048, and. 032, respectively). The post-hoc analysis revealed that the QSM values increased at t1 in the caudate nucleus (66.8 ± 18.0 vs 76.4 ± 16.6, p =. 037) and decreased from t1 to t2 in the putamen (69.4 ± 16.3 vs 62.5 ± 13.6, p =. 025). Changes in the QSM values for the pulvinar and caudate nuclei at t1 were positively and negatively correlated with symptomatic improvement, respectively (r = 0.361 and -0.466, respectively). Conclusions: Intravenous iron treatment results in changes in brain iron content which correlate to reductions in RLS severity. This suggests a connection between symptom improvement and the associated specific brain regions constituting the sensorimotor network

Altered insular functional connectivity in isolated REM sleep behavior disorder: a data-driven functional MRI study

Objective: Functional connectivity (FC) changes can occur prior to structural changes. This study aimed to evaluate data-driven whole-brain FC associated with isolated rapid eye movement sleep behavior disorder (iRBD) using multivariate pattern analysis (MVPA). Methods: This was a cross-sectional study of 50 polysomnography-confirmed iRBD patients and 20 age- and sex-matched controls. We used MVPA implemented in the connectome-MVPA CONN toolbox to identify data-driven seed regions for post hoc seed-to-voxel connectivity analysis. The association between FC changes and clinical characteristics, including cognition, depression, autonomic function, and daytime sleepiness, was evaluated. Results: MVPA revealed one significant cluster located in the left posterior insular cortex. Seed-to-voxel FC analysis using the cluster as a seed showed significantly reduced FC with two clusters located in the precuneus in iRBD patients compared to the controls. The degree of FC was associated with the Montreal Cognitive Assessment-Korean version scores (r = 0.317, p = 0.025). Conclusion: This study emphasizes the insula as an important neural correlate associated with iRBD that was associated with cognitive function