Tailoring mSiO2-SmCox nanoplatforms for magnetic/photothermal effect-induced hyperthermia therapy

Hyperthermia therapy is a hotspot because of its minimally invasive treatment process and strong targeting effect. Herein, a synergistic magnetic and photothermal therapeutic nanoplatform is rationally constructed. The well-dispersive mSiO2-SmCox nanoparticles (NPs) were synthesized through a one-step procedure with the regulated theoretical molar ratio of Sm/Co among 1:1, 1:2, and 1:4 for controlling the dispersion and magnetism properties of SmCox NPs in situ growth in the pore structure of mesoporous SiO2 (mSiO2), where mSiO2 with diverse porous structures and high specific surface areas serving for locating the permanent magnetic SmCox NPs. The mSiO2-SmCox (Sm/Co = 1:2) NPs with highly dispersed and uniform morphology has an average diameter of ∼73.08 nm. The photothermal conversion efficiency of mSiO2-SmCox (Sm/Co = 1:2) NPs was determined to be nearly 41%. The further in vitro and in vivo anti-tumor evaluation of mSiO2-SmCox (Sm/Co = 1:2) NPs present promising potentials for hyperthermia-induced tumor therapy due to magnetic and photothermal effects

Low levels of vitamin C in dialysis patients is associated with decreased prealbumin and increased C-reactive protein

<p>Abstract</p> <p>Background</p> <p>Subclinical inflammation is a common phenomenon in patients on either continuous ambulatory peritoneal dialysis (CAPD) or maintenance hemodialysis (MHD). We hypothesized that vitamin C had anti-inflammation effect because of its electron offering ability. The current study was designed to test the relationship of plasma vitamin C level and some inflammatory markers.</p> <p>Methods</p> <p>In this cross-sectional study, 284 dialysis patients were recruited, including 117 MHD and 167 CAPD patients. The demographics were recorded. Plasma vitamin C was measured by high-performance liquid chromatography. And we also measured body mass index (BMI, calculated as weight/height²), Kt/V, serum albumin, serum prealbumin, high-sensitivity C-reactive protein (hsCRP), ferritin, hemoglobin. The relationships between vitamin C and albumin, pre-albumin and hsCRP levels were tested by Spearman correlation analysis and multiple regression analysis.</p> <p>Patients were classified into three subgroups by vitamin C level according to previous recommendation <abbrgrp><abbr bid="B1">1</abbr><abbr bid="B2">2</abbr></abbrgrp> in MHD and CAPD patients respectively: group A: < 2 ug/ml (< 11.4 umol/l, deficiency), group B: 2-4 ug/ml (11.4-22.8 umol/l, insufficiency) and group C: > 4 ug/ml (> 22.8 umol/l, normal and above).</p> <p>Results</p> <p>Patients showed a widely distribution of plasma vitamin C levels in the total 284 dialysis patients. Vitamin C deficiency (< 2 ug/ml) was present in 95(33.45%) and insufficiency (2-4 ug/ml) in 88(30.99%). 73(25.70%) patients had plasma vitamin C levels within normal range (4-14 ug/ml) and 28(9.86%) at higher than normal levels (> 14 ug/ml). The similar proportion of different vitamin C levels was found in both MHD and CAPD groups.</p> <p>Plasma vitamin C level was inversely associated with hsCRP concentration (Spearman r = -0.201, P = 0.001) and positively associated with prealbumin (Spearman r = 0.268, P < 0.001), albumin levels (Spearman r = 0.161, P = 0.007). In multiple linear regression analysis, plasma vitamin C level was inversely associated with log₁₀hsCRP (P = 0.048) and positively with prealbumin levels (P = 0.002) adjusted for gender, age, diabetes, modality of dialysis and some other confounding effects.</p> <p>Conclusions</p> <p>The investigation indicates that vitamin C deficiency is common in both MHD patients and CAPD patients. Plasma vitamin C level is positively associated with serum prealbumin level and negatively associated with hsCRP level in both groups. Vitamin C deficiency may play an important role in the increased inflammatory status in dialysis patients. Further studies are needed to determine whether inflammatory status in dialysis patients can be improved by using vitamin C supplements.</p

The Impact of High-Temperature Stress on Gut Microbiota and Reproduction in Siberian Hamsters (<i>Phodopus sungorus</i>)

Global warming has induced alterations in the grassland ecosystem, such as elevated temperatures and decreased precipitation, which disturb the equilibrium of these ecosystems and impact various physiological processes of grassland rodents, encompassing growth, development, and reproduction. As global warming intensifies, the repercussions of high-temperature stress on small mammals are garnering increased attention. Recently, research has highlighted that the composition and ratio of gut microbiota are not only shaped by environmental factors and the host itself but also reciprocally influence an array of physiological functions and energy metabolism in animals. In this research, we combined 16S rRNA high-throughput sequencing with conventional physiological assessments, to elucidate the consequences of high-temperature stress on the gut microbiota structure and reproductive capacity of Siberian hamsters (Phodopus sungorus). The results were as follows: 1. The growth and development of male and female hamsters in the high-temperature group were delayed, with lower body weight and reduced food intake. 2. High temperature inhibits the development of reproductive organs in both female and male hamsters. 3. High temperature changes the composition and proportion of gut microbiota, reducing bacteria that promote reproduction, such as Pseudobutyricoccus, Ruminiclostridium-E, Sporofaciens, UMGS1071, and CAG_353. Consequently, our study elucidates the specific impacts of high-temperature stress on the gut microbiota dynamics and reproductive health of Siberian hamsters, thereby furnishing insights for managing rodent populations amidst global climatic shifts. It also offers a valuable framework for understanding seasonal variations in mammalian reproductive strategies, contributing to the broader discourse on conservation and adaptation under changing environmental conditions

Comprehensive use of a high-frequency electric knife, balloon dilatation, and cryotherapy for tuberculous central tracheobronchial cicatricial constriction

Abstract Background To examine the benefits of interventional therapy for cicatricial constriction using a high-frequency electric knife, saccular dilatation, and cryotherapy. Methods This case series included patients with central tracheobronchial cicatricial constriction admitted to the Department of Tuberculosis of Henan Provincial Chest Hospital from July 2018 to March 2021 and treated with bronchoscopic interventional therapies based on systemic anti-tuberculosis treatment. Results 96 patients were included, in whom 443 interventional therapies were performed. The total mid-(3 months) and long-term (12 months) effective rates were both 100%. The internal diameter of tracheobronchial stenosis increased after the operation, and the difference was statistically significant (all < 0.05). After interventional treatment, patients’ symptoms of choking sensation in the chest and shortness of breath were relieved. Respiratory function was obviously improved. The ratios of hemorrhage, granulation hyperplasia, chest pain, and postoperative fever were 58.2%, 42.6%, 31.3%, and 26.7%, respectively. No focal transmission and progression of tuberculosis occurred, and no serious complications were observed. Conclusion The use of a high-frequency electric knife, saccular dilatation, and/or cryotherapy according to the pathological stage of the tracheobronchial cicatricial constriction is feasible, with good mid- and long-term curative effects and few complications

Deep learning for non-parameterized MEMS structural design

The geometric designs of MEMS devices can profoundly impact their physical properties and eventual performances. However, it is challenging for researchers to rationally consider a large number of possible designs, as it would be very time- and resource-consuming to study all these cases using numerical simulation. In this paper, we report the use of deep learning techniques to accelerate the MEMS design cycle by quickly and accurately predicting the physical properties of numerous design candidates with vastly different geometric features. Design candidates are represented in a nonparameterized, topologically unconstrained form using pixelated black-and-white images. After sufficient training, a deep neural network can quickly calculate the physical properties of interest with good accuracy without using conventional numerical tools such as finite element analysis. As an example, we apply our deep learning approach in the prediction of the modal frequency and quality factor of disk-shaped microscale resonators. With reasonable training, our deep learning neural network becomes a high-speed, high-accuracy calculator: it can identify the flexural mode frequency and the quality factor 4.6 × 103 times and 2.6 × 104 times faster, respectively, than conventional numerical simulation packages, with good accuracies of 98.8 ± 1.6% and 96.8 ± 3.1%, respectively. When simultaneously predicting the frequency and the quality factor, up to ~96.0% of the total computation time can be saved during the design process. The proposed technique can rapidly screen over thousands of design candidates and promotes experience-free and data-driven MEMS structural designs