Effect of laser irradiation on cell function and its implications in Raman spectroscopy

Lasers are instrumental in advanced bioimaging and Raman spectroscopy. However, they are also well known for their destructive effects on living organisms, leading to concerns about the adverse effects of laser technologies. To implement Raman spectroscopy for cell analysis and manipulation, such as Raman activated cell sorting, it is crucial to identify non-destructive conditions for living cells. Here, we evaluated quantitatively the effect of 532 nm laser irradiation on bacterial cell fate and growth at the single-cell level. Using a purpose-built microfluidic platform, we were able to quantify the growth characteristics i.e. specific growth rate and lag time of individual cells as well as the survival rate of a population in conjunction with Raman spectroscopy. Representative Gram-negative and Gram-positive species show a similar trend in response to laser irradiation dose. Laser irradiation could compromise physiological function of cells and the degree of destruction is both dose and strain dependent, ranging from reduced cell growth to a complete loss of cell metabolic activity and finally to physical disintegration. Gram-positive bacterial cells are more susceptible than Gram-negative bacterial strains to irradiation-induced damage. By directly correlating Raman acquisition with single cell growth characteristics, we provide evidence of non-destructive characteristics of Raman spectroscopy on individual bacterial cells. However, while strong Raman signals can be obtained without causing cell death, the variety of responses from different strains and from individual cells justify careful evaluation of Raman acquisition conditions if cell viability is critical

In vivo microelectrode arrays for detecting multi-region epileptic activities in the hippocampus in the latent period of rat model of temporal lobe epilepsy

Temporal lobe epilepsy (TLE) is a form of refractory focal epilepsy, which includes a latent period and a chronic period. Microelectrode arrays capable of multi-region detection of neural activities are important for accurately identifying the epileptic focus and pathogenesis mechanism in the latent period of TLE. Here, we fabricated multi-shank MEAs to detect neural activities in the DG, hilus, CA3, and CA1 in the TLE rat model. In the latent period in TLE rats, seizures were induced and changes in neural activities were detected. The results showed that induced seizures spread from the hilus and CA3 to other areas. Furthermore, interneurons in the hilus and CA3 were more excited than principal cells and exhibited rhythmic oscillations at approximately 15 Hz in grand mal seizures. In addition, the power spectral density (PSD) of neural spikes and local field potentials (LFPs) were synchronized in the frequency domain of the alpha band (9–15 Hz) after the induction of seizures. The results suggest that fabricated MEAs have the advantages of simultaneous and precise detection of neural activities in multiple subregions of the hippocampus. Our MEAs promote the study of cellular mechanisms of TLE during the latent period, which provides an important basis for the diagnosis of the lesion focus of TLE

New insights on hyperglycemia in 17-hydroxylase/17,20-lyase deficiency

ObjectiveThe adrenal glands of patients with 17-hydroxylase/17,20-lyase deficiency (17OHD) synthesize excessive 11-deoxycorticosterone(DOC) and progesterone, and produce less amount of sex steroid production. Mineralocorticoids and sex hormones play an important role in regulating glucose homeostasis. This study aimed to describe the glucose metabolism in 17OHD patients diagnosed at Peking Union Medical College Hospital (PUMCH).Design/methodsA total of 69 patients diagnosed with 17OHD after adolescence in PUMCH from 1995 to June in 2021. Among them 23 patients underwent a 3-hours oral glucose tolerance test (3hOGTT) after being diagnosed with 17OHD. Insulin response in patients with normal glucose tolerance (NGT) were further compared between the study two groups with different kalemia status. Another 19 patients were followed up to 30 years and older. All clinical data were obtained from the hospital information system of PUMCH.ResultsBaseline: (1) The average body mass index(BMI) of all patients at baseline was 20.3 ± 3.7kg/m2. Twenty-three patients underwent 3hOGTT, of whom three were diagnosed with diabetes mellitus, and one with impaired glucose tolerance (IGT). Positive correlation between the ratio of progesterone to upper limit of normal range (P times) and hyperglycaemia was exist(r=0.707, P=0.005). (2) In 19 NGT patients, the insulin concentrations at 0 minute, results of the homeostasis model assessment for β-cell function and insulin resistance were lower in the hypokalaemia group than in the normal kalemia group(7.0(5.8-13.2) vs 12.4(8.9-14.9) μIU/ml, P=0.017; 115.5(88.2-240.9) vs 253.1(177.2-305.8), P=0.048; 1.54(1.17-2.61) vs 2.47(1.91-2.98), P=0.022, respectively). Follow-up: Four patients had IGT, while seven patients had diabetes mellitus. Of the 19 patients,11 had hyperglycaemia. P times was significantly higher(7.6(5.0-11.0) vs 3.75(2.2-5.3), P=0.008) in hyperglycemia group than in the normal glucose group.ConclusionsAbnormal glucose metabolism was common in 17OHD patients, which was possibly associated with hypokalaemia and high progesterone levels. Routine monitoring on glucose metabolism in 17OHD patient should be conducted

A global product of fine-scale urban building height based on spaceborne lidar

Characterizing urban environments with broad coverages and high precision is more important than ever for achieving the UN's Sustainable Development Goals (SDGs) as half of the world's populations are living in cities. Urban building height as a fundamental 3D urban structural feature has far-reaching applications. However, so far, producing readily available datasets of recent urban building heights with fine spatial resolutions and global coverages remains a challenging task. Here, we provide an up-to-date global product of urban building heights based on a fine grid size of 150 m around 2020 by combining the spaceborne lidar instrument of GEDI and multi-sourced data including remotely sensed images (i.e., Landsat-8, Sentinel-2, and Sentinel-1) and topographic data. Our results revealed that the estimated method of building height samples based on the GEDI data was effective with 0.78 of Pearson's r and 3.67 m of RMSE in comparison to the reference data. The mapping product also demonstrated good performance as indicated by its strong correlation with the reference data (i.e., Pearson's r = 0.71, RMSE = 4.60 m). Compared with the currently existing products, our global urban building height map holds the ability to provide a higher spatial resolution (i.e., 150 m) with a great level of inherent details about the spatial heterogeneity and flexibility of updating using the GEDI samples as inputs. This work will boost future urban studies across many fields including climate, environmental, ecological, and social sciences

Development and validation of questionnaire-based machine learning models for predicting all-cause mortality in a representative population of China

BackgroundConsidering that the previously developed mortality prediction models have limited applications to the Chinese population, a questionnaire-based prediction model is of great importance for its accuracy and convenience in clinical practice.MethodsTwo national cohort, namely, the China Health and Nutrition Survey (8,355 individual older than 18) and the China Health and Retirement Longitudinal Study (12,711 individuals older than 45) were used for model development and validation. One hundred and fifty-nine variables were compiled to generate predictions. The Cox regression model and six machine learning (ML) models were used to predict all-cause mortality. Finally, a simple questionnaire-based ML prediction model was developed using the best algorithm and validated.ResultsIn the internal validation set, all the ML models performed better than the traditional Cox model in predicting 6-year mortality and the random survival forest (RSF) model performed best. The questionnaire-based ML model, which only included 20 variables, achieved a C-index of 0.86 (95%CI: 0.80–0.92). On external validation, the simple questionnaire-based model achieved a C-index of 0.82 (95%CI: 0.77–0.87), 0.77 (95%CI: 0.75–0.79), and 0.79 (95%CI: 0.77–0.81), respectively, in predicting 2-, 9-, and 11-year mortality.ConclusionsIn this prospective population-based study, a model based on the RSF analysis performed best among all models. Furthermore, there was no significant difference between the prediction performance of the questionnaire-based ML model, which only included 20 variables, and that of the model with all variables (including laboratory variables). The simple questionnaire-based ML prediction model, which needs to be further explored, is of great importance for its accuracy and suitability to the Chinese general population

Influence of nitrogen on corrosion behaviour of high nitrogen martensitic stainless steels manufactured by pressurized metallurgy

Effect of nitrogen on microstructure and corrosion behaviour of high nitrogen martensitic stainless steels manufactured by pressurized metallurgy was investigated by microscopy, electrochemical and spectroscopy analyses. Results indicated that increasing nitrogen content significantly enhanced the corrosion properties of martensitic stainless steels, while excess nitrogen deteriorated the corrosion resistance. The impacts of increased nitrogen content could be summarized as three aspects: the change of precipitation content and conversion of main precipitates from MC to MN; the enhanced protection performance of passive film by enrichment of Cr, especially CrO and CrN; the improved repassivation ability by increased nitrogen content in solid solution

Artificial intelligence-aided rapid and accurate identification of clinical fungal infections by single-cell Raman spectroscopy

Integrating artificial intelligence and new diagnostic platforms into routine clinical microbiology laboratory procedures has grown increasingly intriguing, holding promises of reducing turnaround time and cost and maximizing efficiency. At least one billion people are suffering from fungal infections, leading to over 1.6 million mortality every year. Despite the increasing demand for fungal diagnosis, current approaches suffer from manual bias, long cultivation time (from days to months), and low sensitivity (only 50% produce positive fungal cultures). Delayed and inaccurate treatments consequently lead to higher hospital costs, mobility and mortality rates. Here, we developed single-cell Raman spectroscopy and artificial intelligence to achieve rapid identification of infectious fungi. The classification between fungi and bacteria infections was initially achieved with 100% sensitivity and specificity using single-cell Raman spectra (SCRS). Then, we constructed a Raman dataset from clinical fungal isolates obtained from 94 patients, consisting of 115,129 SCRS. By training a classification model with an optimized clinical feedback loop, just 5 cells per patient (acquisition time 2 s per cell) made the most accurate classification. This protocol has achieved 100% accuracies for fungal identification at the species level. This protocol was transformed to assessing clinical samples of urinary tract infection, obtaining the correct diagnosis from raw sample-to-result within 1 h

Ex Situ Reconstruction-Shaped Ir/CoO/Perovskite Heterojunction for Boosted Water Oxidation Reaction

The oxygen evolution reaction (OER) is the performance-limiting step in the process of water splitting. In situ electrochemical conditioning could induce surface reconstruction of various OER electrocatalysts, forming reactive sites dynamically but at the expense of fast cation leaching. Therefore, achieving simultaneous improvement in catalytic activity and stability remains a significant challenge. Herein, we used a scalable cation deficiency-driven exsolution approach to ex situ reconstruct a homogeneous-doped cobaltate precursor into an Ir/CoO/perovskite heterojunction (SCI-350), which served as an active and stable OER electrode. The SCI-350 catalyst exhibited a low overpotential of 240 mV at 10 mA cm-2 in 1 M KOH and superior durability in practical electrolysis for over 150 h. The outstanding activity is preliminarily attributed to the exponentially enlarged electrochemical surface area for charge accumulation, increasing from 3.3 to 175.5 mF cm-2. Moreover, density functional theory calculations combined with advanced spectroscopy and 18O isotope-labeling experiments evidenced the tripled oxygen exchange kinetics, strengthened metal-oxygen hybridization, and engaged lattice oxygen oxidation for O-O coupling on SCI-350. This work presents a promising and feasible strategy for constructing highly active oxide OER electrocatalysts without sacrificing durability