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Oral squamous cell carcinoma (OSCC) is often diagnosed at late stage with a poor prognosis. The study hereunder aimed to construct a multi-gene model to simultaneously promote early diagnosis of OSCC by evaluating malignant risk of oral potentially malignant disorders (OPMDs) and predict prognosis.

3 GEO datasets including OPMDs and OSCC samples were obtained for overlapping differentially expressed genes (DEGs) being screened. The predictive model was built with optimal DEGs by SVM algorithm, estimated by receiver operator characteristic curves and validated for double prediction via oral cancer-free survival (for malignant risk of OPMDs) and overall survival time (for OSCC) analysis respectively compared to other models. The protein expression of biomarkers in the model was validated in human samples by immunohistochemistry.

A novel predictive model of 4-gene signature was built based on 12 common DEGs revealed from 3 GEO datasets. It could well distinguish OSCC from OPMDs and normal tissues. Both oral cancer-free survival and overall survival time analysis were significantly poorer in high-risk patients than in low-risk ones in Kaplan Meier survival curve respectively. The protein expression of biomarkers in OSCC was with significant difference compared to normal and OPMDs.

The novel 4-gene signature model presents strong ability in simultaneous prediction of the malignant risk of OPMDs and OSCC progression, potentially benefiting both the early diagnosis and therapeutic outcomes of OSCC.
The novel 4-gene signature model presents strong ability in simultaneous prediction of the malignant risk of OPMDs and OSCC progression, potentially benefiting both the early diagnosis and therapeutic outcomes of OSCC.
The 10q26 subtelomeric microdeletion syndrome is a rare and clinically heterogeneous disorder. The precise relationships between the causative genes and the phenotype are unclear.

We report two new cases of 860kb deletion of 10q26.2 identified by array CGH in a fetus with intrauterine growth retardation and his mother. The deleted region encompassed only four coding genes, DOCK1, INSYN2, NPS and FOX12. The proband had dysmorphic facies characterized by a high forehead, malformed ears, a prominent nose, and retrognathia. He had bilateral club feet, clinodactily and mild psychomotor retardation. His mother had a short stature, microcephaly, a long face with a high forehead and bitemporal narrowing, arched and sparse eyebrows, strabismus, prominent nose and chin, a thin upper lip and large protruding ears, and mild intellectual disability.

This study presents the smallest 10q26.2 deletion so far identified, which further refines the minimal critical region associated with the 10q26 microdeletion syndrome. It focuses on three genes potentially responsible for the phenotype DOCK1, which is the major candidate gene, and INSYN2 and NPS, which could be involved in cognitive functions.
This study presents the smallest 10q26.2 deletion so far identified, which further refines the minimal critical region associated with the 10q26 microdeletion syndrome. It focuses on three genes potentially responsible for the phenotype DOCK1, which is the major candidate gene, and INSYN2 and NPS, which could be involved in cognitive functions.
The presence of tertiary lymphoid structures (TLSs) may confer survival benefit to patients with pancreatic ductal adenocarcinoma (PDAC), in an otherwise immunologically inert malignancy. Yet, the precise role in PDAC has not been elucidated. Here, we aim to investigate the structure and role of TLSs in human and murine pancreatic cancer.

Multicolor immunofluorescence and immunohistochemistry were used to fully characterize TLSs in human and murine (transgenic [KPC (Kras
, p53
, Pdx-1-Cre)] and orthotopic) pancreatic cancer. An orthotopic murine model was developed to study the development of TLSs and the effect of the combined chemotherapy and immunotherapy on tumor growth.

Mature, functional TLSs are not ubiquitous in human PDAC and KPC murine cancers and are absent in the orthotopic murine model. TLS formation can be induced in the orthotopic model of PDAC after intratumoral injection of lymphoid chemokines (CXCL13/CCL21). Coadministration of systemic chemotherapy (gemcitabine) and intratumoral lymphoid chemokines into orthotopic tumors altered immune cell infiltration ,facilitating TLS induction and potentiating antitumor activity of chemotherapy. This resulted in significant tumor reduction, an effect not achieved by either treatment alone. Antitumor activity seen after TLS induction is associated with B cell-mediated dendritic cell activation.

This study provides supportive evidence that TLS induction may potentiate the antitumor activity of chemotherapy in a murine model of PDAC. Tanespimycin A detailed understanding of TLS kinetics and their induction, owing to multiple host and tumor factors, may help design personalized therapies harnessing the potential of immune-oncology.
This study provides supportive evidence that TLS induction may potentiate the antitumor activity of chemotherapy in a murine model of PDAC. A detailed understanding of TLS kinetics and their induction, owing to multiple host and tumor factors, may help design personalized therapies harnessing the potential of immune-oncology.Potentially, the toxicity of multiwalled carbon nanotubes (MWCNTs) can be reduced in a safe-by-design strategy. We investigated if genotoxicity and pulmonary inflammation of MWCNTs from the same batch were lowered by a) reducing length and b) introducing COOH-groups into the structure. Mice were administered 1) long and pristine MWCNT (CNT-long) (3.9 μm); 2) short and pristine CNT (CNT-short) (1 μm); 3) CNT modified with high ratio COOH-groups (CNT-COOH-high); 4) CNT modified with low ratio COOH-groups (CNT-COOH-low). MWCNTs were dosed by intratracheal instillation at 18 or 54 μg/mouse (∼0.9 and 2.7 mg/kg bw). Neutrophils numbers were highest after CNT-long exposure, and both shortening the MWCNT and addition of COOH-groups lowered pulmonary inflammation (day 1 and 28). Likewise, CNT-long induced genotoxicity, which was absent with CNT-short and after introduction of COOH groups. In conclusion, genotoxicity and pulmonary inflammation of MWCNTs were lowered, but not eliminated, by shortening the fibres or introducing COOH-groups.
Anti-osteoporotic drug (AOD) trials have variabilities in duration and fracture risks. This study evaluated AOD's versus controls regarding reduction in relative rates (rr) and rate differences (rd) of vertebral and hip fractures and comparative costs.

Primary randomized-controlled trials (RCT's) of AOD's in post-menopausal women with documentation of vertebral fracture rates (VFR) or hip fracture rates (HFR) were extracted from meta-analyses and PubMed through February 2021. Direct and indirect meta-analysis and meta-regression analyzed fracture reductions.

There were 24 RCT's of drug-versus-placebo (73,862 women) and 10 drug-versus-drug trials. Reduction in rr of VFR were significant for anti-resorptive (alendronate, risedronate, zoledronate, denosumab, raloxifene) and anabolic (teriparatide, abaloparatide, romosozumab) drugs. Denosumab, teriparatide and abaloparatide were more effective in reducing VFR compared to oral bisphosphates (all p <0.05) but not to zoledronate. Reduction in HFR were signimpared to oral bisphosphonates. Anabolic drugs are not superior to zoledronate or denosumab, and at substantially higher cost. In comparing drugs which prevented hip fractures, there was no statistical benefit of any drug.This research develops safety performance functions and identifies the crash hotspots based on estimated vulnerable road users' exposure at intersections and along the roadway segments. The study utilized big data including Automated Traffic Signal Performance Measures (ATSPM) data, crowdsourced data (Strava), Closed Circuit Television (CCTV) surveillance camera videos, crash data, traffic information, roadway features, land use attributes, and socio-demographic characteristics. It comprises an extensive comparison between a wide array of statistical and machine learning models that were developed to estimate pedestrian and bike exposure. The results indicated that the XGBoost approach was the best to estimate vulnerable road users' exposure at intersections as well as bike exposure along the roadway segments. Afterwards, the estimated exposure was utilized as input variables to develop crash prediction models that relate different crash types to potential explanatory variables. Negative Binomial approach was followed to develop crash prediction models to be consistent with the Highway Safety Manual. The results show that the exposure variables (i.e., AADT, bike exposure, and the interaction between them) have significant influences on the two types of crashes (i.e., crashes of vulnerable road users at intersections and bike crashes along the segments). Further, the results indicated that the context classification is significantly related to crashes. Based on the developed models, the PSIs were calculated and the hotspots were identified for the two crash types. It was found that hotspots were more likely to be located near the city of Orlando. Coastal roadways were classified as cold categories regarding bike crashes. Further, C4 roadway segments were found to be significantly related to the increase of vulnerable road users' crashes at intersections and bike crashes along the segments.The primary objective of this study was to evaluate the impacts of traffic states on crash risk in the vicinities of Type A weaving segments. A deep convolutional embedded clustering (DCEC) was developed to classify traffic flow into nine states. The proposed DCEC outperformed the three common clustering algorithms, i.e. K-means, deep embedded clustering, and deep convolutional autoencoders clustering, in terms of silhouette coefficient and calinski-harabaz index on the same samples, suggesting that the DCEC provides better clustering performance. The characteristics of the nine traffic states are described for the right and inside lanes separately. The DCED visualization indicates that the spatiotemporal features of the nine traffic states are different from each other. The empirical analyses suggest that crash severity and the main types of crashes are different across the nine traffic states. The results of the logistic regression model prove that the nine traffic states are significantly associated with crash risk in the vicinities of weaving segments, and each traffic state can be assigned with a unique safety level. The convolutional neural network with gated convolutional layers (G-CNN) was developed to predict the crash risk in each traffic state. Compared with the traditional four traffic states classification based on 4-phase traffic theory, the model incorporating the various crash mechanisms across the nine traffic states provides more accurate predictions.Transfusion therapy is a critical part of supportive care early after allogeneic hematopoietic cell transplantation (allo-HCT). Platelet and RBC transfusions elicit immunomodulatory effects in the recipient, but if this impacts the risk of acute graft-versus-host disease (aGVHD) has only been scarcely investigated. We investigated if platelet and RBC transfusions were associated with the development of aGVHD following myeloablative allo-HCT in a cohort of 664 patients who underwent transplantation between 2000 and 2019. Data were further analyzed for the impact of blood donor age and sex and blood product storage time. Exploratory analyses were conducted to assess correlations between transfusion burden and plasma biomarkers of inflammation and endothelial activation and damage. Between day 0 and day +13, each patient received a median of 7 (IQR, 5 to 10) platelet transfusions and 3 (IQR, 2 to 6) RBC transfusions (Spearman's ρ = 0.49). The cumulative sums of platelet and RBC transfusions, respectively, received from day 0 to day +13 were associated with subsequent grade II-IV aGVHD in multivariable landmark Cox models (platelets adjusted hazard ratio [HR], 1.
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