The X(3960)X(3960), X0(4140)X_0(4140), and other cscˉsˉcs\bar{c}\bar{s} compact states

We study the spectrum and rearrangement decays of S-wave $cs\bar{c}\bar{s}$ tetraquark states in a simplified quark model. The masses and widths are estimated by assuming that the $X(4140)$ is the lower $1^{++}$ $cs\bar{c}\bar{s}$ tetraquark. Comparing our results with experimental measurements, we find that the recently observed $X(3960)$ by LHCb can be assigned as the lowest $0^{++}$ $cs\bar{c}\bar{s}$ tetraquark state and the $X_0(4140)$ could be the second lowest $0^{++}$ $cs\bar{c}\bar{s}$ tetraquark. Predictions of ratios between partial widths for the involved tetraquarks are given. We call for searches for more $cs\bar{c}\bar{s}$ tetraquarks with $J^{PC}=1^{+-}$, $0^{++}$, and $2^{++}$

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

The Efficacy of Pretreatment F-FDG PET-CT-Based Deep Learning Network Structure to Predict Survival in Nasopharyngeal Carcinoma

Background: Previous studies have shown that the 5-year survival rates of patients with nasopharyngeal carcinoma (NPC) were still not ideal despite great improvement in NPC treatments. To achieve individualized treatment of NPC, we have been looking for novel models to predict the prognosis of patients with NPC. The objective of this study was to use a novel deep learning network structural model to predict the prognosis of patients with NPC and to compare it with the traditional PET-CT model combining metabolic parameters and clinical factors. Methods: A total of 173 patients were admitted to 2 institutions between July 2014 and April 2020 for the retrospective study; each received a PET-CT scan before treatment. The least absolute shrinkage and selection operator (LASSO) was employed to select some features, including SUVpeak-P, T3, age, stage II, MTV-P, N1, stage III and pathological type, which were associated with overall survival (OS) of patients. We constructed 2 survival prediction models: an improved optimized adaptive multimodal task (a 3D Coordinate Attention Convolutional Autoencoder and an uncertainty-based jointly Optimizing Cox Model, CACA-UOCM for short) and a clinical model. The predictive power of these models was assessed using the Harrell Consistency Index (C index). Overall survival of patients with NPC was compared by Kaplan–Meier and Log-rank tests. Results: The results showed that CACA-UOCM model could estimate OS (C index, 0.779 for training, 0.774 for validation, and 0.819 for testing) and divide patients into low and high mortality risk groups, which were significantly associated with OS ( P  < .001). However, the C-index of the model based only on clinical variables was only 0.42. Conclusions: The deep learning network model based on 18 F-FDG PET/CT can serve as a reliable and powerful predictive tool for NPC and provide therapeutic strategies for individual treatment

Stimulation of BDNF expression by transplanted OPCs.

<p>(A, C) BDNF⁺ cells were visualized by immunolabeling in the cortex and SGZ 7 days after transplantation. (B, D) The number of BDNF⁺ cells was increased in HI animals (HI+PBS) compared to sham-operated controls; injection of OPCs (HI+OPC) potentiated this effect. Data are expressed as mean ± SD. *P < 0.05, HI group vs. sham controls; ^#P < 0.05, ^##P < 0.01 between HI+PBS and HI+OPC groups; n = 6–8 animals per group. Scale bar = 50 μm.</p

Migration of transplanted OPCs in the brain.

<p>GFP⁺ OPCs in the brains of rats subjected to sham operation or HI, then injected with OPCs. Animals were sacrificed 14 days and 6 weeks after transplantation, and coronal sections were immunostained for GFP and counterstained with DAPI. (A, C) GFP-expressing tranplanted OPCs in lateral ventricles and SVZ 14 days after transplantation. (B, D, F) GFP⁺ cells were observed in the regions surrounding the ventricles 6 weeks after transplantation. GFP⁺ OPCs were detected in (E, G) the cortex ispilateral to the side of injury and in the septofimbrial nucleus of the hippocampus and (H, I) in the SGZ of the hippocampus. Scale bar = 50 μm.</p

OPC transplantation inhibits apoptosis in the DG following HI 7 days after transplantation.

<p>(A) Apoptotic neurons (white arrows) were visualized by double immunolabeling with antibodies against caspase-3 (red) and NeuN (green). (B) The number of caspase-3⁺/NeuN⁺ apoptotic cells increased in HI animals (HI+PBS) compared to sham-operated controls; injection of OPCs (HI+OPC) mitigated this effect. Data are expressed as mean ± SD. *P < 0.05, **P < 0.01, HI group vs. sham controls; ^#P < 0.05, ^##P < 0.01 between HI+PBS and HI+OPC groups; n = 6–8 animals per group. Scale bar = 50 μm.</p

OPC transplantation improves spatial learning and memory deficits induced by HI.

<p>Animals were tested in the Morris water maze 36–40 days after transplantation. (A) Number of platform crossings; (B) percentage of time spent in the platform quadrant; (C) escape latency; (D) percentage of distance travelled in the platform quadrant. HI induced cognitive deficits in animals (HI+PBS), including longer escape latency, fewer platform crossings, and decreased time spent and distance traveled in the platform quadrant compared to sham animals; OPC transplantation partly reversed these effects. Data are expressed as mean ± SD. *P <0.05, **P < 0.01, HI group vs. sham controls. ^#P < 0.05, ^##P < 0.01 between HI+PBS and HI+OPC groups; n = 17 animals per group.</p

Survival and differentiation of transplanted OPCs.

<p>(A–D) Apoptosis of OPCs was analyzed labeling cells with DAPI (blue), GFP (green), and by the TUNEL reaction (red). Scale bar = 50 μm. (E–H) OPC differentiation into astrocytes was visualized by DAPI staining (blue) and GFP (green) and GFAP (red) immunolabeling. (I–L) OPC differentiation into neurons was visualized by DAPI staining (blue) and GFP (green) and NeuN (red) immunolabeling. (M–P) Myelin sheath formed by GFP⁺ OPCs was visualized by DAPI staining (blue), and GFP (green) and MBP (red) immunolabeling. Scale bar = 20 μm (E–P).</p