Nevin Manimala

Commun Med (Lond). 2022 Oct 31;2(1):136. doi: 10.1038/s43856-022-00191-8.

ABSTRACT

BACKGROUND: During the COVID-19 pandemic there has been a strong interest in forecasts of the short-term development of epidemiological indicators to inform decision makers. In this study we evaluate probabilistic real-time predictions of confirmed cases and deaths from COVID-19 in Germany and Poland for the period from January through April 2021.

METHODS: We evaluate probabilistic real-time predictions of confirmed cases and deaths from COVID-19 in Germany and Poland. These were issued by 15 different forecasting models, run by independent research teams. Moreover, we study the performance of combined ensemble forecasts. Evaluation of probabilistic forecasts is based on proper scoring rules, along with interval coverage proportions to assess calibration. The presented work is part of a pre-registered evaluation study.

RESULTS: We find that many, though not all, models outperform a simple baseline model up to four weeks ahead for the considered targets. Ensemble methods show very good relative performance. The addressed time period is characterized by rather stable non-pharmaceutical interventions in both countries, making short-term predictions more straightforward than in previous periods. However, major trend changes in reported cases, like the rebound in cases due to the rise of the B.1.1.7 (Alpha) variant in March 2021, prove challenging to predict.

CONCLUSIONS: Multi-model approaches can help to improve the performance of epidemiological forecasts. However, while death numbers can be predicted with some success based on current case and hospitalization data, predictability of case numbers remains low beyond quite short time horizons. Additional data sources including sequencing and mobility data, which were not extensively used in the present study, may help to improve performance.

PMID:36352249 | DOI:10.1038/s43856-022-00191-8

Eur J Pediatr. 2022 Nov 10. doi: 10.1007/s00431-022-04675-6. Online ahead of print.

ABSTRACT

Phototherapy is the main treatment of neonatal hyperbilirubinemia to prevent encephalopathy. It is generally believed to be safe; however, some studies have shown it might be associated with cancer development. In this systematic review and meta-analysis, we aimed to assess the effect of neonatal phototherapy on future cancer risk. A systematic search in 13 databases was conducted in December 2018 and updated in August 2022 to identify studies that report cancer development after exposure to phototherapy. Throughout the study period, regular manual searches were also conducted to include new studies. A meta-analysis using R programming language was done in which the odds ratios (ORs) with 95% confidence intervals (CIs) were estimated and pooled using the reported adjusted and unadjusted data. Fifteen studies were included. A statistically significant association was detected between neonatal phototherapy and any type of cancer (OR 1.24; 95% CI 1.1, 1.4), any hematopoietic cancer (OR 1.49; 95% CI 1.17, 1.91), any leukemia (OR 1.35; 95% CI 1.08, 1.67), and myeloid leukemia (OR 2.86; 95% CI 1.4, 5.84). The other investigated cancers (lymphoid leukemia, Hodgkin’s lymphoma, kidney cancer, nervous system cancer, and skin cancer) were not associated with phototherapy. Conclusions: Phototherapy may carry a possible risk of future cancers. Future research is needed to quantify the magnitude of the cancer risk. These future studies should consider predictors of preterm birth or exclude premature babies from their analysis. What is Known • There were various reports about the possible association between phototherapy in neonates and the increased risk of cancer in the future. What is New • A statistically significant association between phototherapy and various hematopoietic cancers (especially myeloid leukemia) was recorded. • The effect of the duration of phototherapy on the increased risk of hematopoietic cancers is yet unclear.

PMID:36352244 | DOI:10.1007/s00431-022-04675-6

Hum Genet. 2022 Nov 9. doi: 10.1007/s00439-022-02505-1. Online ahead of print.

ABSTRACT

Mitochondrial DNA (mtDNA) plays a critical role in oocyte maturation, fertilization, and early embryonic development. Defects in mtDNA may determine the alteration of the mitochondrial function, affecting cellular oxidative phosphorylation and ATP supply, leading to impaired oocyte maturation, abnormal fertilization, and low embryonic developmental potential, ultimately leading to female infertility. This case-control study was established to investigate the correlation between mtDNA variations and early embryonic development defects. Peripheral blood was collected for next-generation sequencing from women who suffered the repeated failures of in vitro fertilization (IVF) and/or intracytoplasmic sperm injection (ICSI) cycles due to early embryonic development defects as well as in-house healthy controls, and the sequencing results were statistically analyzed for all subjects. This study found that infertile women with early embryonic development defects carried more mtDNA variants, especially in the D-loop region, ATP6 gene, and CYTB gene. By univariate logistic regression analysis, 16 mtDNA variants were associated with an increased risk of early embryonic development defects (OR > 1, p < 0.05). Furthermore, we identified 16 potentially pathogenic mtDNA variants only in infertile cases. The data proved that mtDNA variations were associated with early embryonic development defects in infertile Chinese women.

PMID:36352239 | DOI:10.1007/s00439-022-02505-1

Med Oncol. 2022 Nov 9;40(1):15. doi: 10.1007/s12032-022-01873-y.

ABSTRACT

Acute lymphoblastic leukemia (ALL) is hematological neoplasia that affects human beings from early life to adulthood. Although ALL treatment has been effective, an important percentage of ALL patients are resilient to treatment. Therefore, there is an urgent need for testing a new combination of compounds for the treatment of this disease. Recently, combined TPEN and TPGS (T2 combo) have shown selective cytotoxic effects in vitro leukemia cells such as Jurkat, K562, and Ba/F3 cells. In this study, we aimed to test the effect of combined TPEN and TPGS agents (T2 combo) at a fixed dose (TPEN 5 mg/kg: TPGS 100 mg/kg) on leukemic Ba/F3-BCR-ABL P210 BALB-c mice model. We found that 4 successive 2-day apart intravenous injections of T2 combo showed a statistically significant reduction of Ba/F3 BCR-ABL leukemia cells (- 69%) in leukemia BALB/c mice (n = 6) compared to untreated leukemia group (n = 6). Moreover, the T2 combo was innocuous to non-leukemia BALB/c mice (n = 3) compared to untreated non-leukemia mice (control, n = 3). After treatments (day 42), all mice were left to rest until day 50. Outstandingly, the leukemia BALB/c mice treated with the T2 combo showed a lower percentage of Ba/F3-BCR-ABL P210 cells (- 84%) than untreated leukemia BALB/c mice. Furthermore, treatment of leukemia and non-leukemia mice with T2 combo showed no significant tissue alteration/damage according to the histopathological analysis of brain, heart, liver, kidney, and spleen samples; however, T2 combo significantly reduced the number of leukocytes in the bone marrow of treated leukemia mice. We conclude that the T2 combo specifically affects leukemia cells but no other tissue/organs. Therefore, we anticipate that the T2 combo might be a potential pro-oxidant combination for the treatment of leukemia patients.

PMID:36352172 | DOI:10.1007/s12032-022-01873-y

Environ Monit Assess. 2022 Nov 10;195(1):93. doi: 10.1007/s10661-022-10710-8.

ABSTRACT

GIS is a very powerful tool for analyzing huge amount of data and connecting them with the geography; moreover, recently, there is great advancement in the field. The main objective of this study is to assess the water quality (WQ) and trophic status (TS) conditions of Lake Burullus, Egypt, using statistical modeling (PCA/FA and CA), WQ index (L-WQI), and trophic status index (Carlson TSI and TRIX) approaches, in addition to using GIS tools for building models able to automatically calculate the various indices and producing color coded maps for the lake. The results indicated that PCA/FA grouped the twenty-four WQ parameters into nine principal components explaining 72.6% of the total variance, domestic, and agriculture pollution were dominant. CA divided the twelve sampling stations into most and least polluted groups. The lake WQ was classified as a “Very Poor,” according to L-WQI. Moreover, the results of the Carlson TSI and TRIX indices were coincided and classified the eutrophication levels in the lake as “Hyper-Eutrophic” and “Elevated Trophic,” respectively. Based on the results of this study, Lake Burullus needs urgent plans for recovering its WQ. Pre-treatment for its drains’ effluents and implementing of a periodical WQ monitoring program are highly recommended.

PMID:36352171 | DOI:10.1007/s10661-022-10710-8

J Digit Imaging. 2022 Nov 9. doi: 10.1007/s10278-022-00737-1. Online ahead of print.

ABSTRACT

Automated co-registration and subtraction techniques have been shown to be useful in the assessment of longitudinal changes in multiple sclerosis (MS) lesion burden, but the majority depend on T2-fluid-attenuated inversion recovery sequences. We aimed to investigate the use of a novel automated temporal color complement imaging (CCI) map overlapped on 3D double inversion recovery (DIR), and to assess its diagnostic performance for detecting disease progression in patients with multiple sclerosis (MS) as compared to standard review of serial 3D DIR images. We developed a fully automated system that co-registers and compares baseline to follow-up 3D DIR images and outputs a pseudo-color RGB map in which red pixels indicate increased intensity values in the follow-up image (i.e., progression; new/enlarging lesion), blue-green pixels represent decreased intensity values (i.e., disappearing/shrinking lesion), and gray-scale pixels reflect unchanged intensity values. Three neuroradiologists blinded to clinical information independently reviewed each patient using standard DIR images alone and using CCI maps based on DIR images at two separate exams. Seventy-six follow-up examinations from 60 consecutive MS patients who underwent standard 3 T MR brain MS protocol that included 3D DIR were included. Median cohort age was 38.5 years, with 46 women, 59 relapsing-remitting type MS, and median follow-up interval of 250 days (interquartile range: 196-394 days). Lesion progression was detected in 67.1% of cases using CCI review versus 22.4% using standard review, with a total of 182 new or enlarged lesions using CCI review versus 28 using standard review. There was a statistically significant difference between the two methods in the rate of all progressive lesions (P < 0.001, McNemar’s test) as well as cortical progressive lesions (P < 0.001). Automated CCI maps using co-registered serial 3D DIR, compared to standard review of 3D DIR alone, increased detection rate of MS lesion progression in patients undergoing clinical brain MRI exam.

PMID:36352165 | DOI:10.1007/s10278-022-00737-1

Commun Biol. 2022 Nov 9;5(1):1203. doi: 10.1038/s42003-022-04092-3.

ABSTRACT

Classic bladder exstrophy represents the most severe end of all human congenital anomalies of the kidney and urinary tract and is associated with bladder cancer susceptibility. Previous genetic studies identified one locus to be involved in classic bladder exstrophy, but were limited to a restrict number of cohort. Here we show the largest classic bladder exstrophy genome-wide association analysis to date where we identify eight genome-wide significant loci, seven of which are novel. In these regions reside ten coding and four non-coding genes. Among the coding genes is EFNA1, strongly expressed in mouse embryonic genital tubercle, urethra, and primitive bladder. Re-sequence of EFNA1 in the investigated classic bladder exstrophy cohort of our study displays an enrichment of rare protein altering variants. We show that all coding genes are expressed and/or significantly regulated in both mouse and human embryonic developmental bladder stages. Furthermore, nine of the coding genes residing in the regions of genome-wide significance are differentially expressed in bladder cancers. Our data suggest genetic drivers for classic bladder exstrophy, as well as a possible role for these drivers to relevant bladder cancer susceptibility.

PMID:36352089 | DOI:10.1038/s42003-022-04092-3

NPJ Digit Med. 2022 Nov 9;5(1):164. doi: 10.1038/s41746-022-00696-5.

ABSTRACT

Physical health status defines an individual’s ability to perform normal activities of daily living and is usually assessed in clinical settings by questionnaires and/or by validated tests, e.g. timed walk tests. These measurements have relatively low information content and are usually limited in frequency. Wearable sensors, such as activity monitors, enable remote measurement of parameters associated with physical activity but have not been widely explored beyond measurement of daily step count. Here we report on results from a cohort of 22 individuals with Pulmonary Arterial Hypertension (PAH) who were provided with a Fitbit activity monitor (Fitbit Charge HR^®) between two clinic visits (18.4 ± 12.2 weeks). At each clinical visit, a maximum of 26 measurements were recorded (19 categorical and 7 continuous). From analysis of the minute-to-minute step rate and heart rate we derive several metrics associated with physical activity and cardiovascular function. These metrics are used to identify subgroups within the cohort and to compare to clinical parameters. Several Fitbit metrics are strongly correlated to continuous clinical parameters. Using a thresholding approach, we show that many Fitbit metrics result in statistically significant differences in clinical parameters between subgroups, including those associated with physical status, cardiovascular function, pulmonary function, as well as biomarkers from blood tests. These results highlight the fact that daily step count is only one of many metrics that can be derived from activity monitors.

PMID:36352062 | DOI:10.1038/s41746-022-00696-5

Phys Med Biol. 2022 Nov 9. doi: 10.1088/1361-6560/aca194. Online ahead of print.

ABSTRACT

OBJECTIVE: Deep neural network (DNN) based methods have shown promising performances for low-dose computed tomography (LDCT) imaging. However, most of the DNN-based methods are trained on simulated labeled datasets, and the low-dose simulation algorithms are usually designed based on simple statistical models which deviate from the real clinical scenarios, which could lead to issues of overfitting, instability and poor robustness. To address these issues, in this work, we present a structure-preserved meta-learning uniting network (shorten as “SMU-Net”) to suppress noise-induced artifacts and preserve structure details in the unlabeled LDCT imaging task in real scenarios.

APPROACH: Specifically, the presented SMU-Net contains two networks, i.e., teacher network and student network. The teacher network is trained on simulated labeled dataset and then helps the student network train with the unlabeled LDCT images via themeta-learning strategy. The student network is trained on real LDCT dataset with the pseudo-labels generated by the teacher network. Moreover, the student network adopts the Co-teaching strategy to improve the robustness of the presented SMU-Net.

MAIN RESULTS: We validate the proposed SMU-Net method on three public datasets and one real low-dose dataset. The visual image results indicate that the proposed SMU-Net has superior performance on reducing noise-induced artifacts and preserving structure details. And the quantitative results exhibit that the presented SMU-Netmethod generally obtains the highest signal-to-noise ratio (PSNR), the highest structural similarity index measurement (SSIM), and the lowest root-mean-square error (RMSE) values or the lowest natural image quality evaluator (NIQE) scores.

SIGNIFICANCE: We propose a meta learning strategy to obtain high-quality CT images in the LDCT imaging task, which is designed to take advantage of unlabeled CT images to promote the reconstruction performance in the LDCT environments.

PMID:36351294 | DOI:10.1088/1361-6560/aca194

Inorg Chem. 2022 Nov 9. doi: 10.1021/acs.inorgchem.2c03225. Online ahead of print.

ABSTRACT

Atomically dispersed organometallic clusters can provide well-defined nuclearity of active sites for both fundamental studies as well as new regimes of activity and selectivity in chemical transformations. More recently, dinuclear clusters adsorbed onto solid surfaces have shown novel catalytic properties resulting from the synergistic effect of two metal centers to anchor different reactant species. Difficulty in synthesizing, stabilizing, and characterizing isolated atoms and clusters without agglomeration challenges allocating catalytic performance to atomic structure. Here, we explore the stability of dinuclear rhodium and iridium clusters adsorbed onto layered titanate and niobate supports using molecular precursors. Both systems maintain their nuclearity when characterized using aberration-corrected high-angle annular dark-field scanning transmission electron microscopy (HAADF-STEM). Statistical analysis of HAADF-STEM images revealed that rhodium and iridium dimers had mean cluster-to-cluster distances very similar to what is expected from a random distribution of atoms over a large area, indicating that they are dispersed without aggregation. The stability of dinuclear rhodium clusters supported on titanate nanosheets was also investigated by X-ray absorption fine structure (EXAFS), DRIFTS, and first-principles calculations. Both X-ray absorption spectroscopy and HAADF-STEM simulations, guided by density functional theory (DFT)-optimized structure models, suggested that rhodium dimers adsorb onto the nanosheets in an end-on binding mode that is stable up to 100 °C under reducing conditions. This study highlights that crystalline nanosheets derived from layered metal oxides can be used as model supports to selectively stabilize dinuclear clusters, which could have implications for heterogeneous catalysis.

PMID:36351259 | DOI:10.1021/acs.inorgchem.2c03225