Cerebral oxygen desaturation occurs frequently in patients with hypertension undergoing major abdominal surgery

Hypertensive patients are more likely to experience latent cerebral ischemia causing regional cerebral oxygen saturation (rSO2) decrease during general anesthesia. The aim of this prospective observational study was to assess the incidence of decreased rSO2 in hypertensive patients undergoing major abdominal surgery and the perioperative factors affecting this change in rSO2. A total of 41 hypertensive patients were enrolled and stratified according to their hypertension as controlled and uncontrolled. The intraoperative rSO2 and physiological data were routinely collected. The Mini-Mental State Exam (MMSE) was used to test cognitive function before surgery and after 4 days. Cerebral desaturation was defined as a decrease in rSO2 of more than 20% of the baseline value. There were 20 patients (49%) suffering intraoperative cerebral desaturation classified into cerebral desaturation group (group D) and those 21 without intraoperative desaturation classified into normal group (group N). The area under the curve below 90 and 80% of baseline (AUCrSO2 <90% of baseline and AUCrSO2 <80% of baseline) was lower in patients of group N (2752.4 ± 1453.3 min% and 0.0 min%) than in patients of group D (6264.9 ± 1832.3 min% and 4486.5 ± 1664.9 min%, P < 0.001). Comparing the two groups, the number of uncontrolled hypertensive individuals in group D (12/20) was significantly more than group N (4/21) (P = 0.007). A significant correlation was observed between relative decrease in MAP and relative decrease in rSO2 (r2 = 0.495, P < 0.001). Moreover, nine patients (45%) in group D occurred early postoperative cognitive function decline were more than three patients (14.3%) in group N (P = 0.031). This pilot study showed a large proportion of hypertensive patient experienced cerebral desaturation during major abdominal surgery and uncontrolled hypertension predisposed to this desaturation. NCT02147275 (registered at http://www.clinicaltrials.gov)

Homo-binding character of LMO2 isoforms and their both synergic and antagonistic functions in regulating hematopoietic-related target genes

<p>Abstract</p> <p>Background</p> <p>The human <it>lmo2 </it>gene plays important roles in hematopoiesis and is associated with acute T lymphocyte leukemia. The gene encodes two protein isoforms, a longer form LMO2-L and a shorter form LMO2-S. Both isoforms function as bridge molecules to assemble their partners together to regulate their target genes. A typical LMO2 binding site consists of two elements, a GATA site and an E-box, with an interval of 9~12 bp.</p> <p>Methods</p> <p>In this study, the combination of MBP pulldown assay and mammalian two hybrid assay were used to confirm the homo-binding character of LMO2-L/-S isoforms. Luciferase reporter assay and Real-time PCR assay were used to detect expression levels and relative promoter activities of LMO2-L/-S isoforms. Co-transfection and Luciferase reporter assay were used to reveal the detailed regulatory pattern of LMO2-L/-S isoforms on their targets.</p> <p>Results</p> <p>Herein we report the homo-interaction character of LMO2-L and LMO2-S and their major difference in manner of regulating their target genes. Our results showed that LMO2-L and LMO2-S could only bind to themselves but not each other. It was also demonstrated that LMO2-L could either positively or negatively regulate the transcription of its different target genes, depending on the arrangement and strand location of the two elements GATA site and E-box, LMO2-S, however, performed constitutively transcriptional inhibiting function on all target genes.</p> <p>Conclusion</p> <p>These results suggest that LMO2 isoforms have independent functions while there is no interaction between each other and they could play synergetic or antagonistic roles precisely in regulating their different genes involved in normal and aberrant hematopoiesis.</p

Time trend analysis of thyroid cancer surgery in China: single institutional database analysis of 15,000 patients

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Effect of 15 days −6° head-down bed rest on microbial communities of supragingival plaque in young men

IntroductionThe microgravity environment astronauts experience during spaceflight can lead to an increased risk of oral diseases and possible changes in oral microecology. In this study, we aimed to assess changes in the microbial community of supragingival plaques to explore the effects of spaceflight microgravity environment on oral microecology.MethodsSixteen healthy male volunteers were recruited, and supragingival plaque samples were collected under −6° head-down bed rest (HDBR) at five-time points: day 1 before HDBR; days 5, 10, and 15 of HDBR; and day 6 of recovery. Bacterial genomic DNA was sequenced using gene sequencing technology with 16S ribosomal ribonucleic acid V3–V4 hypervariable region amplification and the obtained data were analyzed bioinformatically.ResultsAlpha diversity analysis showed a significant increase in species richness in supragingival plaque samples on day 15 of HDBR compared with that at pre-HDBR. Beta diversity analysis revealed that the community composition differed among the groups. Species distribution showed that, compared with those at pre-HDBR, the relative abundances of Corynebacterium and Aggregatibacter increased significantly during HDBR, while those of Veillonella, Streptococcus, and Lautropia decreased significantly. Moreover, compared with those at pre-HDBR, the relative abundance of Leptotrichia increased significantly on day 6 of recovery, whereas the relative abundances of Porphyromonas and Streptococcus decreased significantly. Network analysis showed that the interaction relationship between the dominant genera became simpler during HDBR, and the positive and negative correlations between them showed dynamic changes. Phylogenetic investigation of communities by reconstruction of unobserved states analysis showed that the amino acid metabolism function of plaque microorganisms was more enriched during HDBR.DiscussionIn summary, in a 15-day simulated microgravity environment, the diversity, species distribution, interaction relationship, and metabolic function of the supragingival plaque microbial community changed, which suggests that microgravity may affect the oral microecosystem by changing the balance of supragingival plaque microbial communities and further leading to the occurrence and development of oral diseases

STU-Net: Scalable and Transferable Medical Image Segmentation Models Empowered by Large-Scale Supervised Pre-training

Large-scale models pre-trained on large-scale datasets have profoundly advanced the development of deep learning. However, the state-of-the-art models for medical image segmentation are still small-scale, with their parameters only in the tens of millions. Further scaling them up to higher orders of magnitude is rarely explored. An overarching goal of exploring large-scale models is to train them on large-scale medical segmentation datasets for better transfer capacities. In this work, we design a series of Scalable and Transferable U-Net (STU-Net) models, with parameter sizes ranging from 14 million to 1.4 billion. Notably, the 1.4B STU-Net is the largest medical image segmentation model to date. Our STU-Net is based on nnU-Net framework due to its popularity and impressive performance. We first refine the default convolutional blocks in nnU-Net to make them scalable. Then, we empirically evaluate different scaling combinations of network depth and width, discovering that it is optimal to scale model depth and width together. We train our scalable STU-Net models on a large-scale TotalSegmentator dataset and find that increasing model size brings a stronger performance gain. This observation reveals that a large model is promising in medical image segmentation. Furthermore, we evaluate the transferability of our model on 14 downstream datasets for direct inference and 3 datasets for further fine-tuning, covering various modalities and segmentation targets. We observe good performance of our pre-trained model in both direct inference and fine-tuning. The code and pre-trained models are available at https://github.com/Ziyan-Huang/STU-Net

Role of Protein Charge Density on Hepatitis B Virus Capsid Formation

The role of electrostatic interactions in the viral capsid assembly process was studied by comparing the assembly process of a truncated hepatitis B virus capsid protein Cp149 with its mutant protein D2N/D4N, which has the same conformational structure but four fewer charges per dimer. The capsid protein self-assembly was investigated under a wide range of protein surface charge densities by changing the protein concentration, buffer pH, and solution ionic strength. Lowering the protein charge density favored the capsid formation. However, lowering charge beyond a certain point resulted in capsid aggregation and precipitation. Interestingly, both the wild-type and D2N/D4N mutant displayed identical assembly profiles when their charge densities matched each other. These results indicated that the charge density was optimized by nature to ensure an efficient and effective capsid proliferation under the physiological pH and ionic strength

A-Eval: A Benchmark for Cross-Dataset Evaluation of Abdominal Multi-Organ Segmentation

Although deep learning have revolutionized abdominal multi-organ segmentation, models often struggle with generalization due to training on small, specific datasets. With the recent emergence of large-scale datasets, some important questions arise: \textbf{Can models trained on these datasets generalize well on different ones? If yes/no, how to further improve their generalizability?} To address these questions, we introduce A-Eval, a benchmark for the cross-dataset Evaluation ('Eval') of Abdominal ('A') multi-organ segmentation. We employ training sets from four large-scale public datasets: FLARE22, AMOS, WORD, and TotalSegmentator, each providing extensive labels for abdominal multi-organ segmentation. For evaluation, we incorporate the validation sets from these datasets along with the training set from the BTCV dataset, forming a robust benchmark comprising five distinct datasets. We evaluate the generalizability of various models using the A-Eval benchmark, with a focus on diverse data usage scenarios: training on individual datasets independently, utilizing unlabeled data via pseudo-labeling, mixing different modalities, and joint training across all available datasets. Additionally, we explore the impact of model sizes on cross-dataset generalizability. Through these analyses, we underline the importance of effective data usage in enhancing models' generalization capabilities, offering valuable insights for assembling large-scale datasets and improving training strategies. The code and pre-trained models are available at \href{https://github.com/uni-medical/A-Eval}{https://github.com/uni-medical/A-Eval}