Sequential Monitoring Research Articles

(249) Association of Single Nucleotide Polymorphisms (rs2301365 & Rs737008) in PRM1 Gene and Male Infertility: A Meta-Analysis

Abstract Introduction In the sperm nucleus, protamine is a major DNA-binding protein that replaces histone. It is a basic arginine-rich protein responsible for DNA compaction. Protamin I (PRM1) is one of the important genes that produces protamine. Mutations and polymorphisms in PRM1 may increase the odds of male infertility. Objective The present meta-analysis investigated the association of single nucleotide polymorphisms (SNPs) (rs2301365 & rs737008) in the PRM1 gene and male infertility. Methods The original research articles, published till July 2023 in the English language, on the association of rs2301365 and rs737008 in the PRM1 gene and male infertility, were retrieved from Science Direct, PubMed, NCBI and Google Scholar. Results A total of 129 studies were retrieved by searching in the different databases, after the removal of duplicates, meta-analysis, and abstracts, 14 were finally selected. In 11 case-control studies, the association of the rs2301365 (-190C >A) polymorphism was presented as the meta-analysis of different models, Allelic model (C vs. A), Dominant model (CC + CA vs. AA), Recessive model (CC vs. CA + AA), Additive model (CC vs. AA), Heterozygous model (AC vs. AA). Allelic, additive, dominant and heterozygous models reported that the -190C>A (rs2301365) increased the odds of male infertility. The ethnic subgroup analysis showed the same pattern of association for the Caucasian infertile men. However, none of these overall and subgroup analyses based on ethnicity showed an association between rs2301365 (-190C >A) and infertility in Asian men. For rs737008 (139C > A) in the meta-analysis of 14 studies, none of these overall and subgroup analyses based on ethnicity showed an association with male infertility. Trial sequential analysis (TSA) for the allelic model of rs2301365 (-190C >A) showed the required sample size was 2252 at 90% power and 5% type-I error, whereas the cumulative z-curve crossed the trial sequential monitoring boundaries of harm. However, the sample size was greater than required therefore further studies will not likely change results. For rs737008 (139C > A), TSA recommended more studies to be conducted. Conclusions The PRM1 rs2301365 (-190C >A) was associated with male infertility in overall analysis and Caucasian men. Further studies on this SNP may not change results. However, rs737008 (139C > A) was not associated with male infertility. However, further studies are recommended. Disclosure No.

Prevalence of frequent premature ventricular contractions and nonsustained ventricular tachycardia in older women screened for atrial fibrillation in the Women’s Health Initiative

BackgroundFrequent premature ventricular contractions (PVCs) and nonsustained ventricular tachycardia (NSVT) have been associated with cardiovascular disease and mortality. Their prevalence, especially in ambulatory populations, is understudied and limited by few female participants and the use of short-duration (24- to 48-hour) monitoring. ObjectiveThe objective of this study was to report the prevalence of frequent PVCs and NSVT in a community-based population of women likely to undergo electrocardiogram (ECG) screening by sequential patch monitoring. MethodsParticipants from the Women’s Health Initiative Strong and Healthy (WHISH) trial with no history of atrial fibrillation (AF) but 5-year predicted risk of incident AF ≥5% by CHARGE-AF score were randomly selected to undergo screening with 7-day ECG patch monitors at baseline, 6 months, and 12 months. Recordings were reviewed for PVCs and NSVT (>5 beats); data were analyzed with multivariate regression models. ResultsThere were 1067 participants who underwent ECG screening at baseline, 866 at 6 months, and 777 at 12 months. Frequent PVCs were found on at least 1 patch from 4.3% of participants, and 1 or more episodes of NSVT were found in 12 (1.1%) women. PVC frequency directly correlated with CHARGE-AF score and NSVT on any patch. Detection of frequent PVCs increased with sequential monitoring. ConclusionIn postmenopausal women at high risk for AF, frequent PVCs were relatively common (4.3%) and correlated with higher CHARGE-AF score. As strategies for AF screening continue to evolve, particularly in those individuals at high risk of AF, the prevalence of incidental ventricular arrhythmias is an important benchmark to guide clinical decision-making.

Early Identification of Geological Disasters in Loess Hilly Area of Ningnan Based on InSAR Technology: A case study of Yuanzhou District, China

Using small baseline subset InSAR (SBAS-InSAR) technology and multi-temporal synthetic aperture radar data, multitemporal phase and long-term sequential deformation monitoring and early identification of geological hazards were carried out in the loess hilly area of southern Ningnan-Yuanzhou area. Based on the analysis of radar visibility in the loess hilly area of southern Ningxia. The Sentinel-1A historical archived data of 36 periods from January 2019–December 2021 and descending orbit data from January 2019–May 2021 were used to carry out monitoring of surface deformation in the river basin, and a total of 61 hidden danger. 49 new concealed high risk points were identified by comprehensive optical remote sensing interpretation. The annual average deformation rate of the radar line-of-sight direction of the ascent data was -47.6746–71.6472mm/a, and the cumulative deformation variable was -81.20–121.61mm. There were 23 new concealed high risk points identified. The annual average deformation rate was -72.1574–51.6028mm/a, and the cumulative deformation variable was -165.6–112.4 mm. During the descending orbit, there were 21 obvious deformations and 16 newly discovered geological hazards. Taking a group of ascending orbit deformation zones in Yuanzhou District as an example, which proved that the application of SBAS-InSAR technology in the loess hilly area of Ningnan was feasible.

Sequential online monitoring for autoregressive time series of counts

Sequential online monitoring for autoregressive time series of counts

Efficacy and safety of thalidomide in gastrointestinal angiodysplasias: systematic review and meta-analysis with trial sequential analysis of randomized controlled trials.

Gastrointestinal (GI) angiodysplasias is a potential cause of life-threatening bleeding. Thalidomide may have a certain effect on the treatment. We aim to evaluate the efficacy and safety of thalidomide and used trial sequential analysis (TSA) to assess the need for further randomized controlled trials (RCTs). Meta-analysis of RCTs. We systematically searched Cochrane Central Register of Controlled Trials (CENTRAL), Medical Literature Analysis and Retrieval System Online (MEDLINE), Embase, WanFang, and China National Knowledge Infrastructure databases for RCTs evaluating thalidomide in GI angiodysplasias without language restrictions. We used a random-effects model to obtain pool data and followed Grading of Recommendations Assessment, Development and Evaluation framework. TSA was employed to control the risk of random errors and to evaluate the validity of our conclusions. Three RCTs were included involving 279 patients with the proportion of small intestinal angiodysplasias of 87.1%. Thalidomide led to improved mean change of hemoglobin level [mean difference (MD): 3.06, 95% confidence interval: 2.66-3.46] without severe adverse effects occurring. Other secondary endpoints, including effective response rate, cessation of bleeding after treatment, hospitalization rate because of bleeding, change in duration of hospital stays for bleeding, transfused red cell requirements, and overall adverse effects, also showed significantly better outcomes in the thalidomide group compared to the control group. TSA for all outcomes exceeded required information sizes, and cumulative Z curve all traverse trial sequential monitoring boundary. Almost all of the evidence was of moderate quality, suggesting that thalidomide holds promise for treating GI angiodysplasias, with favorable safety profiles. TSA suggests that conducting large-scale real-world research is recommended over relying solely on RCTs conducted within the same population and trial design. This meta-analysis protocol was registered on PROSPERO (CRD42023480621).

Sequential Monitoring for Changes in Dynamic Semiparametric Risk Models

Sequential Monitoring for Changes in Dynamic Semiparametric Risk Models

Sequential monitoring of cancer immunotherapy trial with random delayed treatment effect

ABSTRACT Cancer immunotherapy trials are frequently characterized by a delayed treatment effect that violates the proportional hazards assumption. The log-rank test (LRT) suffers a substantial loss of statistical power under the nonproportional hazards model. Various group sequential designs using weighted LRTs (WLRTs) have been proposed under the fixed delayed treatment effect model. However, patients enrolled in immunotherapy trials are often heterogeneous, and the duration of the delayed treatment effect is a random variable. Therefore, we propose group sequential designs under the random delayed effect model using the random delayed distribution WLRT. The proposed group sequential designs are developed for monitoring the efficacy of the trial using the method of Lan-DeMets alpha-spending function with O’Brien-Fleming stopping boundaries or a gamma family alpha-spending function. The maximum sample size for the group sequential design is obtained by multiplying an inflation factor with the sample size for the fixed sample design. Simulations are conducted to study the operating characteristics of the proposed group sequential designs. The robustness of the proposed group sequential designs for misspecifying random delay time distribution and domain is studied via simulations.

Evaluation of Easym, a Clonotypic Mass Spectrometry Assay, and Euroflow Minimal Residual Disease Assessment in Multiple Myeloma

Introduction: Minimal residual disease (MRD) negativity is increasingly recognised as the optimal measure of therapeutic response for multiple myeloma (MM) patients and is associated with improved survival. Bone marrow (BM) assessment of MRD with next-generation sequencing (NGS) or next-generation flow cytometry (NGF) can achieve a minimum sensitivity of 10 -5 however an invasive procedure is required. Additionally, this single-site BM biopsy may be limited by specimen quality and failure to capture the spatial heterogeneity of MM. Peripheral blood-based mass spectrometry (PB-based MS) offers the potential for highly sensitive, non-invasive sequential MRD assessment. EasyM is a PB clonotypic MS assay involving de novo amino acid sequencing of the full-length M-protein and quantification of unique peptides with parallel reaction monitoring. EasyM has been compared to 8-colour multiparameter flow cytometry with a sensitivity of 10 -4 (Liyasova et al, Clin Cancer Res, 2021) and clonoSEQ (Slade et al, Blood, 2022), but not to NGF to our knowledge. In this context, we have evaluated EasyM in MM patients undergoing sequential NGF (EuroFlow platform) MRD assessment. Methods: We retrospectively identified MM patients enrolled in the Australasian Leukaemia and Lymphoma Group MM19 (ACTRN12616000772448) and MM21 (ACTRN12618001490268) trials with measurable M-protein ≥2g/L by serum protein electrophoresis and/or free light chains ≥100mg/L at baseline. Briefly, the MM19 trial evaluated the addition of ixazomib to thalidomide and dexamethasone consolidation therapy for 12 months in transplant eligible newly diagnosed MM (TE NDMM) patients undergoing front-line autologous stem cell transplantation (ASCT), whilst the MM21 trial evaluated an intensive salvage approach using daratumumab-lenalidomide-dexamethasone (DRd) as re-induction (DRd x 4 cycles) and post-ASCT consolidation (DRd x 12 cycles followed by R maintenance until disease progression) in TE NDMM patients failing (<partial response as best response) front-line bortezomib-based induction therapy. NGF MRD status was determined using the standardised 8-colour EuroFlow platform at pre-ASCT, post-ASCT, and end of consolidation timepoints in MM19 and MM21 patients and additionally post-cycle 2 of consolidation in MM21 patients. Matched serum samples were evaluated with the EasyM assay. Concordance between NGF and MS was assessed by the Chi-squared and McNemar's tests. Results: A total of 43 patients were identified for analysis, with 9 MM19 and 34 MM 21 patients. 1 patient was not sequenced due to low baseline values and 2 patients had no clonotypic target tryptic peptides to monitor MRD. EasyM was undetectable (MS-) in 3 (8%) patients. 2 patients achieved MS- pre-ASCT and the third achieved MS- post-cycle 2 of consolidation. All 3 patients remain in complete response (CR) at 44-48 months post-ASCT. Sequential MS (Figure 1) revealed rising EasyM levels in 2 patients, preceding relapse by 15 and 25 months; of note, 1 patient had matched BM and was NGF MRD negative. Serum samples for MS with matched BM for NGF (Table 1) were available for 25 patients pre-ASCT, 32 post-ASCT, 26 post-cycle 2 of consolidation, and 28 end of consolidation with NGF MRD negativity (NGF-) of 8%, 31%, 38%, and 75% at these timepoints, respectively. Of the 111 matched samples in total, 43 (39%) were NGF- but only 7 (6%) were MS-, and 73 (66%) were concordant. The Chi-squared test for association between MS and NGF was not significant at all 4 timepoints. The McNemar's test p-values were >0.999, 0.004, 0.008, and <0.001 for the 4 timepoints, indicating that MS+/NGF- were more likely than MS-/NGF+ discordances at all timepoints except for pre-ASCT. Of the 30 matched samples with confirmed CR status, 5 (17%) were MS+/NGF+, 19 (63%) were MS+/NGF-, and 6 (20%) were MS-/NGF- (Chi-squared and McNemar's p-values of 0.549 and <0.001). Conclusion: This preliminary data highlights the potential of EasyM for sequential PB-based clonotypic MS MRD monitoring in MM. Concordance with standard BM NGF was poor, with 63% of samples with confirmed CR showing detectable M-protein by EasyM but NGF MRD negativity, consistent with the higher sensitivity of EasyM. Comparison of larger sample sets and validation through prospective clinical trials is warranted to better assess the clinical utility of EasyM and rationalise BM-based assessment for MM patients.

Blood Count Scattergrams Are Fingerprints of Blood: Using AI to Inform Health Status

Introduction: Blood count analysers are routinely used to assess health status and for disease monitoring. The major analysers generate more than 300 red cell, leukocyte and platelet parameters. However, in clinical practice, only 10-20 are utilised; a greater number are reviewed by laboratories but some are merely for ensuring instrument stability and quality assurance. Although there are hundreds more measurable parameters available through extended analytical channels, these are not assessed due to the complexity of the numerical analysis and questions as to the clinical utility. However, these extended analytical parameters have potential to be used to generate real-time ‘3-Dimensional (3-D)‘ details of blood cells. Aim: In this study we assessed 312 parameters generated from 5,800 patient samples (of all ages and genders) at a tertiary care hospital (National Institute of Blood Disease, Karachi-Pakistan). These were anonymously outputted and processed by machine learning (ML). Method: The methodology of the present study was based on the waterfall model. The output data from a haematology analyser (Sysmex XN-1000, Kobe Japan) in CSV format, having a total 433 columns, was pre-processed to remove unnecessary features (such as date, analysis details, rack position, receiving time, alerts and others) using Pandas and Numpy libraries, and where required scaling was performed. Data labeling was conducted on conclusions reported on their respective confirmatory tests. The processed data (which included 312 features of the total 433 columns) was labeled (with 67 conclusions on their confirmatory tests). The extracted data was fed to the artificial intelligence Machine Learning models through Python modules and libraries for training, testing, and validation purposes. The web application was developed on modern Python framework (Flask) to automate and provide an option of ‘drag and drop’ the CSV file exported from analyser, we connected pre-processing (data engineering), Machine Learning, and Prediction view by set of different tools (majorly JavaScript libraries). This generated various metrics including accuracy, precision, recall and the mean of precision and recall (F1 score) in relation to the prediction (results) from the CSV file submitted to our system. For each forecast the entire precision report and prediction box were added an optional visualisation on our web panel. Results: Analysis of 1.8 million data points (312 parameters x 5,800 samples) presented promising predictive potential, as, on principal component analysis (PCA) pilot the total variance was remained 41.6% showing that a linear combination of parameters can explain much variability. On a heat map the clustering and visualisation advocated the predictive potential and signatory deviational trends (fingerprints) respectively of these 3-D blood cell features. Examples included separation of myeloid from lymphoid, chronic from acute, bacteria from viral, deficiency of iron from deficiency from vitamin B12 / Folic acid, and differentiation of haemoglobinopathies. The patterns of normal, immature and abnormal blood cells under the title of cell population data was well demonstrated from results of our machine learning models. Of note, we observed an accuracy of 85.6% along with 91.2% precision for one of the ML models used (Random Forest Classifier). Conclusion: The opportunities and challenges of such high dimensional cell population data derived from a complete blood count can provide a novel patient-specific haematological fingerprint. This extended deviational patterning (fingerprint) can provide interpretive diagnostic data with practical disease-specific patterns. This pilot study shows that complete blood count data driven machine learning applications has great potential to uncover disease-associated patterns which could be applied in practice. It also has capacity to provide baseline testing would could assist in sequential health monitoring and potentially the generation of personal reference ranges.

Usefulness of Peripheral Blood Samples in Diagnosis and Prognosis Assessment of Myelodysplastic Syndromes and Chronic Myelomonocytic Leukemia Low Risk Patients Using Next Generation Sequencing

Introduction: myelodysplastic syndromes (MDS) are a heterogeneous group of hematological diseases. Morphologic bone marrow (BM) examination, cytogenetic analysis and molecular techniques are indispensable for accurate diagnosis and disease classification. BM constitutes the most suitable sample for molecular studies, however, peripheral blood (PB) can be obtained by minimally invasive venipuncture and would be ideal for comprehensive sequential monitoring of MDS over time. Aim: to assess whether targeted deep sequencing (TDS) allows the detection of somatic variants from diagnosis and follow-up PB samples and if the molecular profiles are comparable to those from BM samples. Methods: preliminary results include the study of 32 low risk patients (28 MDS and 4 chronic myelomonocytic leukemia, CMML). At least one sample, either BM or PB, was obtained at the moment of diagnosis and whenever possible, paired samples obtained the same day were collected. Over the course of the disease only PB samples were obtained. Libraries were prepared for all available samples using DNA from whole BM or whole PB using a custom hybridization-probe based panel (KAPA HyperCap, Roche), including selected exons of 50 myeloid-related genes. TDS was performed on Illumina MiSeq instrument at a mean coverage of 1000x. Data were analyzed using local bioinformatic pipeline. Only those variants with ≥100x locus coverage and ≥25 reads for the alternative allele (quality criteria) were considered for downstream analysis. Variants were filtered according to variant type, location, read depth (>100x) and population frequency (<1%). Results: patients´ characteristics are described in table 1. The combination of cytogenetics and TDS identified genetic alterations in 29/32 patients (91%). TDS allowed the identification of somatic variants in 27/32 patients (85%), being TET2 (n=23, 29%), SF3B1 (n=12, 15%) and ASXL1 (n=8, 10%) the most frequently mutated genes. Considering molecular information, restratification of IPSS-R to IPSS-M risk groups of MDS patients (n=28) was performed (Table 1). As previously described (Sauta et al, J Clin Oncol, 2023), we performed a five-to-five comparison of IPSS-R and IPSS-M patients' distribution (merging moderate low and moderate high to moderate in IPSS-M). This resulted in the restratification of 36% of MDS patients (10/28) of which 6% (n=2) were downstaged and 25% (n=8) were upstaged. Suitability of PB samples for NGS analysis was evaluated in 21 patients in which paired BM and PB samples were obtained at diagnosis. Overall, 65 variants were found, and 63 of them were detectable in both BM and PB samples. Despite the other two variants were also detected in PB samples at low variant allele frequency (VAF) values ( TET2 and NRAS, VAF=1,5% and 0,99%, respectively), they strictly did not meet our quality criteria (≥25 reads for the alternative allele). Nevertheless, a strong correlation was identified between VAF values for the individual variants detected in BM vs. PB (Figure 1). As part of this project, molecular profile during patients' follow-up is being studied using PB samples. To date, at least one follow-up sample of each patient was studied. The mean time between diagnosis and the first follow-up sample was 28 months (range 6-84 months). After this time, and despite molecular profiles are heterogeneous, variations could be detected in up to 72% of patients (23/32) using PB samples. The most commonly detected variation was an increasing in VAF values (9/23; defined by a difference >5% in relation to the VAF detected in the previous sample), followed by a new variant detection + increasing in VAF values (7/23), new variant detection only (6/23) and a single case where simultaneous decreasing VAF and disappearance of subclones was detected (single patient receiving treatment with hypomethylating agent). Conclusion: molecular testing has become increasingly important for accurate diagnosis and risk assessment in MDS and CMML patients, specially with the new classifications and prognostic scoring system. Here we demonstrate that the molecular analysis from PB cells constitutes an appropriate alternative for the detection and quantification of somatic variants in MDS patients at diagnosis and during disease course. Funding: Bristol Myers Squibb (PI-13279-IIT), FEHH and 2021 SGR 00560 (GRC).

Group sequential two-stage preference designs.

The two-stage preference design (TSPD) enables inference for treatment efficacy while allowing for incorporation of patient preference to treatment. It can provide unbiased estimates for selection and preference effects, where a selection effect occurs when patients who prefer one treatment respond differently than those who prefer another, and a preference effect is the difference in response caused by an interaction between the patient's preference and the actual treatment they receive. One potential barrier to adopting TSPD in practice, however, is the relatively large sample size required to estimate selection and preference effects with sufficient power. To address this concern, we propose a group sequential two-stage preference design (GS-TSPD), which combines TSPD with sequential monitoring for early stopping. In the GS-TSPD, pre-planned sequential monitoring allows investigators to conduct repeated hypothesis tests on accumulated data prior to full enrollment to assess study eligibility for early trial termination without inflating type I error rates. Thus, the procedure allows investigators to terminate the study when there is sufficient evidence of treatment, selection, or preference effects during an interim analysis, thereby reducing the design resource in expectation. To formalize such a procedure, we verify the independent increments assumption for testing the selection and preference effects and apply group sequential stopping boundaries from the approximate sequential density functions. Simulations are then conducted to investigate the operating characteristics of our proposed GS-TSPD compared to the traditional TSPD. We demonstrate the applicability of the design using a study of Hepatitis C treatment modality.

Mobilization of Circulating Tumor Cells after Short- and Long-Term FOLFIRINOX and GEM/nab-PTX Chemotherapy in Xenograft Mouse Models of Human Pancreatic Cancer.

Mobilization of CTCs after various types of therapy, such as radiation therapy, has been reported, but systematic study of CTCs after chemotherapy remained quite limited. In this study, we sequentially examined CTC numbers after single-dose and repetitive-dose chemotherapy, including FORFIRINOX (FFX) and Gemcitabine and nab-Paclitaxel (GnP) using two pancreatic cancer xenograft models. CTC was detected by the immunocytology-based microfluidic platform. We further examined the dynamic change in the histology of primary tumor tissues during chemotherapy. We confirmed a transient increase in CTCs 1-2 weeks after single-dose and repetitive-dose of FFX/GnP chemotherapy. Histological examination of the primary tumors revealed that the peak period of CTC at 1-2 weeks after chemotherapy corresponded to the maximal destructive phase consisting of cell cycle arrest, apoptosis of tumor cells, and blood vessel destruction without secondary reparative tissue reactions and regeneration of tumor cells. These findings indicate that mobilization of CTCs early after chemotherapy is mediated by the shedding of degenerated tumor cells into the disrupted blood vessels driven by the pure destructive histological changes in primary tumor tissues. These results suggest that sequential CTC monitoring during chemotherapy can be a useful liquid biopsy diagnostic tool to predict tumor chemosensitivity and resistance in preclinical and clinical settings.

Recent advances in droplet sequential monitoring methods for droplet sorting.

Droplet microfluidics is an attractive technology to run parallel experiments with high throughput and scalability while maintaining the heterogeneous features of individual samples or reactions. Droplet sorting is utilized to collect the desired droplets based on droplet characterization and in-droplet content evaluation. A proper monitoring method is critical in this process, which governs the accuracy and maximum frequency of droplet handling. Until now, numerous monitoring methods have been integrated in the microfluidic devices for identifying droplets, such as optical spectroscopy, mass spectroscopy, electrochemical monitoring, and nuclear magnetic resonance spectroscopy. In this review, we summarize the features of various monitoring methods integrated into droplet sorting workflow and discuss their suitable condition and potential obstacles in use. We aim to provide a systematic introduction and an application guide for choosing and building a droplet monitoring platform.

Cost aware LSTM model for predicting hard disk drive failures based on extremely imbalanced S.M.A.R.T. sensors data

Failure prediction in a population of hard drives remains challenging due to the extreme class imbalance. Many existing failure prediction methods rely on problematic resampling approaches. Besides, existing deep learning methods in the failure prediction domain typically employ the sigmoid cross-entropy loss function which is not capable of solving the class imbalance problem. To address these challenges, this study proposes long-short term memory networks that leverage the sequential time series self monitoring analysis and reporting technology sensors data provided by the Backblaze data center. These models employ a new, modified focal loss function combined with weighted cross-entropy loss to tackle the negative influence of the class imbalance during training the long-short term memory networks. The long-short term memory networks models are compared to two traditional machine learning algorithms: random forest and logistic regression. This study focuses on daily snapshot data of the Seagate ST4000DM000 hard drive model from the Backblaze data center to maintain the homogeneity of the SMART attributes. Long-short term memory networks with modified focal loss function model provides the highest geometric mean score of 0.786±0.011 while keeping the failure detection rate higher without compromising the false alarm rate. Hence, it demonstrates that the proposed focal loss function in long-short term memory networks leads to improved performance compared to other approaches under the class imbalance.

Germline and somatic drivers in inherited hematologic malignancies.

Inherited hematologic malignancies are linked to a heterogenous group of genes, knowledge of which is rapidly expanding using panel-based next-generation sequencing (NGS) or whole-exome/whole-genome sequencing. Importantly, the penetrance for these syndromes is incomplete, and disease development, progression or transformation has critical clinical implications. With the earlier detection of healthy carriers and sequential monitoring of these patients, clonal hematopoiesis and somatic driver variants become significant factors in determining disease transformation/progression and timing of (preemptive) hematopoietic stem cell transplant in these patients. In this review, we shed light on the detection of probable germline predisposition alleles based on diagnostic/prognostic 'somatic' NGS panels. A multi-tier approach including variant allele frequency, bi-allelic inactivation, persistence of a variant upon clinical remission and mutational burden can indicate variants with high pre-test probability. We also discuss the shared underlying biology and frequency of germline and somatic variants affecting the same gene, specifically focusing on variants in DDX41, ETV6, GATA2 and RUNX1. Germline variants in these genes are associated with a (specific) pattern or over-/underrepresentation of somatic molecular or cytogenetic alterations that may help identify the underlying germline syndrome and predict the course of disease in these individuals. This review is based on the current knowledge about somatic drivers in these four syndromes by integrating data from all published patients, thereby providing clinicians with valuable and concise information.

Soil Pollution Prediction from Enterprise for Coking Industry in China Based on Machine Learning and Scenario Analysis

Owing to the lack of sequential monitoring data of soil pollutants in coking industry enterprises, it is hard to accurately predict their soil pollution. To predict the trend of soil pollution of coking industry enterprises in the future, a prediction model should be developed using machine learning based on the influencing factors. A total of 13 potential factors were selected from the enterprise characteristics, enterprise management level, pollutant characteristics, and natural factors, and the main controlling factors were identified. On this basis, the prediction models were developed using a support vector machine, BP neural network model, decision tree model, and logistic model, and then the pollution situation of enterprises in the coking industry in 2025 and 2030 was predicted under different scenarios. The results indicated that time of service for the enterprise, time of establishment for the enterprise, the environmental illegal record, soil clay, and annual wind speed were the major controlling factors of soil pollution of enterprises in the coking industry. Compared with the support vector machine, BP neural network model, and decision tree model, the logistic model had a robust performance index, with an area under the receiver operating characteristic curve of 0.91. The accurate rate and recall rate were 84% and 88%, respectively. Under the optimistic scenario, there will be 1599 and 1695 plots with a high probability of pollution in the coking industry in 2025 and 2030, respectively; under the pessimistic scenario, there will be 1671 and 1715 plots with a high probability of pollution in the coking industry in 2025 and 2030, respectively. The results of this study provided a scientific basis for soil environmental remediation and eco-environmental strategy development for the coking industry.

Immunology of THymectomy And childhood CArdiac transplant (ITHACA): protocol for a UK-wide prospective observational cohort study to identify immunological risk factors of post-transplant lymphoproliferative disease (PTLD) in thymectomised children

IntroductionPaediatric heart transplant patients are disproportionately affected by Epstein-Barr virus (EBV)-related post-transplant lymphoproliferative disease (PTLD) compared with other childhood solid organ recipients. The drivers for this disparity remain poorly understood....

FeBRILe3: Safety Evaluation of Febrile Infant Guidelines Through Prospective Bayesian Monitoring.

Despite evidence supporting earlier discharge of well-appearing febrile infants at low risk of serious bacterial infection (SBI), admissions for ≥48 hours remain common. Prospective safety monitoring may support broader guideline implementation. A sequential Bayesian safety monitoring framework was used to evaluate a new hospital guideline recommending early discharge of low-risk infants. Hospital readmissions within 7 days of discharge were regularly assessed against safety thresholds, derived from historic rates and expert opinion, and specified a priori (8 per 100 infants). Infants aged under 3 months admitted to 2 Western Australian metropolitan hospitals for management of fever without source were enrolled (August 2019-December 2021), to a prespecified maximum 500 enrolments. Readmission rates remained below the prespecified threshold at all scheduled analyses. Median corrected age was 34 days, and 14% met low-risk criteria (n = 71). SBI was diagnosed in 159 infants (32%), including urinary tract infection (n = 140) and bacteraemia (n = 18). Discharge occurred before 48 hours for 192 infants (38%), including 52% deemed low-risk. At study completion, 1 of 37 low-risk infants discharged before 48 hours had been readmitted (3%), for issues unrelated to SBI diagnosis. In total, 20 readmissions were identified (4 per 100 infants; 95% credible interval 3, 6), with >0.99 posterior probability of being below the prespecified noninferiority threshold, indicating acceptable safety. A Bayesian monitoring approach supported safe early discharge for many infants, without increased risk of readmission. This framework may be used to embed safety evaluations within future guideline implementation programs to further reduce low-value care.

Sequential monitoring of stock market price changes

In 2016, Eugene Fama mentioned that he wanted a systematic way of identifying and predicting a stock market bubble. This paper develops a statistical method to sequentially monitor the stock market price changes. A new simple boundary function is proposed and asymptotic properties are established. In the empirical application, we successfully detect that the U.S. dot-com bubble occurred around June 1997 using the S&P 500 and Nasdaq indices.

Incisional negative pressure wound therapy for the prevention of surgical site infection: an up-to-date meta-analysis and trial sequential analysis

The evidence on prophylactic use of negative pressure wound therapy on primary closed incisional wounds (iNPWT) for the prevention of surgical site infections (SSI) is confusing and ambiguous. Implementation in daily practice is impaired by inconsistent recommendations in current international guidelines and published meta-analyses. More recently, multiple new randomised controlled trials (RCTs) have been published. We aimed to provide an overview of all meta-analyses and their characteristics; to conduct a new and up-to-date systematic review and meta-analysis and Grading of Recommendations Assessment, Development and Evaluation (GRADE) assessment; and to explore the additive value of new RCTs with a trial sequential analysis (TSA). PubMed, Embase and Cochrane CENTRAL databases were searched from database inception to October 24, 2022. We identified existing meta-analyses covering all surgical specialties and RCTs studying the effect of iNPWT compared with standard dressings in all types of surgery on the incidence of SSI, wound dehiscence, reoperation, seroma, hematoma, mortality, readmission rate, skin blistering, skin necrosis, pain, and adverse effects of the intervention. We calculated relative risks (RR) with corresponding 95% confidence intervals (CI) using a Mantel-Haenszel random-effects model. We assessed publication bias with a comparison-adjusted funnel plot. TSA was used to assess the risk of random error. The certainty of evidence was evaluated using the Cochrane Risk of Bias-2 (RoB2) tool and GRADE approach. This study is registered with PROSPERO, CRD42022312995. We identified eight previously published general meta-analyses investigating iNPWT and compared their results to present meta-analysis. For the updated systematic review, 57 RCTs with 13,744 patients were included in the quantitative analysis for SSI, yielding a RR of 0.67 (95% CI: 0.59-0.76, I2=21%) for iNPWT compared with standard dressing. Certainty of evidence was high. Compared with previous meta-analyses, the RR stabilised, and the confidence interval narrowed. In the TSA, the cumulative Z-curve crossed the trial sequential monitoring boundary for benefit, confirming the robustness of the summary effect estimate from the meta-analysis. In this up-to-date meta-analysis, GRADE assessment shows high-certainty evidence that iNPWT is effective in reducing SSI, and uncertainty is less than in previous meta-analyses. TSA indicated that further trials are unlikely to change the effect estimate for the outcome SSI; therefore, if future research is to be conducted on iNPWT, it is crucial to consider what the findings will contribute to the existing robust evidence. Dutch Association for Quality Funds Medical Specialists.