2-Amino-1,3-benzothiazole-6-carboxamide Preferentially Binds the Tandem Mismatch Motif r(UY:GA).

RNA helices are often punctuated with non-Watson-Crick features that may be targeted by chemical compounds, but progress toward identifying such compounds has been slow. We embedded a tandem UU:GA mismatch motif (5'-UG-3':5'-AU-3') within an RNA hairpin stem to identify compounds that bind the motif specifically. The three-dimensional structure of the RNA hairpin and its interaction with a small molecule identified through virtual screening are presented. The G-A mismatch forms a sheared pair upon which the U-U base pair stacks. The hydrogen bond configuration of the U-U pair involves O2 of the U adjacent to the G and O4 of the U adjacent to the A. The G-A and U-U pairs are flanked by A-U and G-C base pairs, respectively, and the stability of the mismatch is greater than when the motif is within the context of other flanking base pairs or when the 5'-3' orientation of the G-A and U-U pairs is swapped. Residual dipolar coupling constants were used to generate an ensemble of structures against which a virtual screen of 64480 small molecules was performed. The tandem mismatch was found to be specific for one compound, 2-amino-1,3-benzothiazole-6-carboxamide, which binds with moderate affinity but extends the motif to include the flanking A-U and G-C base pairs. The finding that the affinity for the UU:GA mismatch is dependent on flanking sequence emphasizes the importance of the motif context and potentially increases the number of small noncanonical features within RNA that can be specifically targeted by small molecules.

Machine learning models identify predictive features of patient mortality across dementia types.

BACKGROUND: Dementia care is challenging due to the divergent trajectories in disease progression and outcomes. Predictive models are needed to flag patients at risk of near-term mortality and identify factors contributing to mortality risk across different dementia types. METHODS: Here, we developed machine-learning models predicting dementia patient mortality at four different survival thresholds using a dataset of 45,275 unique participants and 163,782 visit records from the U.S. National Alzheimer's Coordinating Center (NACC). We built multi-factorial XGBoost models using a small set of mortality predictors and conducted stratified analyses with dementiatype-specific models. RESULTS: Our models achieved an area under the receiver operating characteristic curve (AUC-ROC) of over 0.82 utilizing nine parsimonious features for all 1-, 3-, 5-, and 10-year thresholds. The trained models mainly consisted of dementia-related predictors such as specific neuropsychological tests and were minimally affected by other age-related causes of death, e.g., stroke and cardiovascular conditions. Notably, stratified analyses revealed shared and distinct predictors of mortality across eight dementia types. Unsupervised clustering of mortality predictors grouped vascular dementia with depression and Lewy body dementia with frontotemporal lobar dementia. CONCLUSIONS: This study demonstrates the feasibility of flagging dementia patients at risk of mortality for personalized clinical management. Parsimonious machine-learning models can be used to predict dementia patient mortality with a limited set of clinical features, and dementiatype-specific models can be applied to heterogeneous dementia patient populations.

Dementia has emerged as a major cause of death in societies with increasingly aging populations. However, predicting the exact timing of death in dementia cases is challenging, due to variations in the gradual process where cognitive decline interferes with the body's normal functions. In our study, we build machine-learning models to predict whether a patient diagnosed with dementia will survive or die within 1, 3, 5, or 10 years. We found that the prediction models can work well across patients from different parts of the US and across patients with different types of dementia. The key predictive factor was the information that is already used to diagnose and stage dementia, such as the results of memory tests. Interestingly, broader risk factors related to other causes of death, such as heart conditions, were less significant for predicting death in dementia patients. The ability of these models to identify dementia patients at a heightened risk of mortality could aid clinical practices, potentially allowing for earlier interventions and tailored treatment strategies to improve patient outcomes.

Predictive Models and Features of Patient Mortality across Dementia Types.

Dementia care is challenging due to the divergent trajectories in disease progression and outcomes. Predictive models are needed to identify patients at risk of near-term mortality. Here, we developed machine learning models predicting survival using a dataset of 45,275 unique participants and 163,782 visit records from the U.S. National Alzheimer's Coordinating Center (NACC). Our models achieved an AUC-ROC of over 0.82 utilizing nine parsimonious features for all one-, three-, five-, and ten-year thresholds. The trained models mainly consisted of dementia-related predictors such as specific neuropsychological tests and were minimally affected by other age-related causes of death, e.g., stroke and cardiovascular conditions. Notably, stratified analyses revealed shared and distinct predictors of mortality across eight dementia types. Unsupervised clustering of mortality predictors grouped vascular dementia with depression and Lewy body dementia with frontotemporal lobar dementia. This study demonstrates the feasibility of flagging dementia patients at risk of mortality for personalized clinical management.

Polymorphisms in luteinizing hormone receptor and hypothalamic gonadotropin-releasing hormone genes and their effects on sperm quality traits in Chinese Holstein bulls.

Genes of hypothalamic-pituitary-gonadal axis play a key role in male reproductive performance. This study evaluated the polymorphisms of luteinizing hormone receptor (LHR) and hypothalamic gonadotropin-releasing hormone (GnRH) genes and their effects on sperm quality traits including semen volume per ejaculate (VOL), sperm density (SD), fresh sperm motility (FSM), thawed sperm motility (TSM), acrosome integrity rate (AIR), and abnormal sperm rate (ASR) collected from 205 Chinese Hostein bulls. The study bulls consisted of 205 mature Chinese Holstein, 27 Simmental, 28 Charolais, and 14 German yellow cattle. One single nucleotide polymorphism (SNP) (A883G) in exon 2 of GnRH and two SNPs (A51703G and G51656T) in intron 9 of LHR were identified in 274 bulls. Analysis of variance in 205 Chinese Holstein bulls showed that age had significant effect on both SD and FSM (P < 0.01), and ASR (P < 0.05). With regards to genotype and its interaction with age, only the SNP of G51656T in LHR gene had significant effect on SD (P < 0.05, P < 0.01; respectively). The association result showed that bulls with AG genotype had higher FSM than bulls with AA and GG genotype in LHR at 51,703 locus (P < 0.10), and bulls with GG genotype had higher SD than bulls with TT genotype in LHR at G51656T locus (P < 0.10). Phenotypic correlation among the traits revealed that significant negative correlations were observed between ASR and AIR (r = -0.736, P < 0.01), ASR and AIR (r = -0.500, P < 0.01). There were moderate positive correlations between VOL and SD (r = 0.422, P < 0.01), as well as FSM (r = 0.411, P < 0.01). In conclusion, LHR may be a potential marker for sperm quality of SD and FSM.

Intracavitary electrocardiogram guidance for placement of peripherally inserted central catheters in premature infants.

This study evaluated the accuracy of intracavitary electrocardiogram (IC-ECG) guidance for placement of peripherally inserted central catheters (PICCs) in premature infants, relative to chest X-ray.Premature infants (n = 173) underwent placement of a PICC monitored by ECG, and a stable heart rhythm was shown. Changes in the P wave of the ECG reflected the position of the catheter tip, and a characterized P wave indicated the correct position. The P wave results were compared with a chest X-ray.P wave changes were observed in 157 (90.75%) of the premature infants on the ECG. Among them, the catheter tips of 148 (85.55%) and nine (5.20%) patients were in the correct and non-correct position, respectively, which was confirmed by chest X-ray. No characteristic P wave changes were observed in 16 (9.32%) patients on ECG, in which the catheter tips of eight (4.62%) patients each were in the correct and non-correct position, according to the chest X-ray. The accuracy of IC-ECG guidance for placement of the PICC was 90.17%. The PICC tip location results of the IC-ECGs were statistically similar to that of the chest X-rays.IC-ECG guidance is accurate for placement of PICC in premature infants, and provides an economical assessment without radiation.

The Non-Coding RNA Journal Club: Highlights on Recent Papers-6.

We are delighted to share with you our sixth Journal Club and highlight some of the most interesting papers published recently [...].

A new anterolateral approach for type C fractures of the distal femur.

To provide an anatomic basis for treating type C distal femoral fractures by a new anterolateral approach. Twenty surgical procedures were performed in 10 adult cadaveric specimens using a new anterolateral approach followed by dissection of all lower limbs. The main anterolateral muscles and ligaments were observed. Vessels and nerves related to the new anterolateral approach were also evaluated. Full exposure of the distal femur was achieved. The iliotibial band was protected, and damage to the quadriceps femoris was reduced. The distance between the common peroneal nerve and the new incision line at the level of the lateral epicondyle of the femur was (χ̄ ± s) 8.19 ± 0.79 cm (range, 7.48-9.57 cm). This new anterolateral approach to the distal femur is safe. Although it induces slight soft tissue damage, its exposure is excellent. Knee rehabilitation can be performed in the early postoperative period.