Targeting the superior temporal gyrus with real-time fMRI neurofeedback: A pilot study of the indirect effects on self-referential processes in schizophrenia.

BACKGROUND: Individuals with schizophrenia (SZ) and auditory hallucinations (AHs) display a distorted sense of self and self-other boundaries. Alterations of activity in midline cortical structures such as the prefrontal cortex (mPFC) and anterior cingulate cortex (ACC) during self-reference as well as in the superior temporal gyrus (STG) have been proposed as neuromarkers of SZ and AHs. METHODS: In this randomized, participant-blinded, sham-controlled trial, 22 adults (18 males) with SZ spectrum disorders (SZ or schizoaffective disorder) and frequent medication-resistant AHs received one session of real-time fMRI neurofeedback (NFB) either from the STG (n = 11; experimental group) or motor cortex (n = 11; control group). During NFB, participants were instructed to upregulate their STG activity by attending to pre-recorded sentences spoken in their own voice and downregulate it by ignoring unfamiliar voices. Before and after NFB, participants completed a self-reference task where they evaluated if trait adjectives referred to themselves (self condition), Abraham Lincoln (other condition), or whether adjectives had a positive valence (semantic condition). FMRI activation analyses of self-reference task data tested between-group changes after NFB (self>semantic, post>pre-NFB, experimental>control). Analyses were pre-masked within a self-reference network. RESULTS: Activation analyses revealed significantly (p < 0.001) greater activation increase in the experimental, compared to the control group, after NFB within anterior regions of the self-reference network (mPFC, ACC, superior frontal cortex). CONCLUSIONS: STG-NFB was associated with activity increase in the mPFC, ACC, and superior frontal cortex during self-reference. Modulating the STG is associated with activation changes in other, not-directly targeted, regions subserving higher-level cognitive processes associated with self-referential processes and AHs psychopathology in SZ. CLINICALTRIALS: GOV: Rt-fMRI Neurofeedback and AH in Schizophrenia; https://clinicaltrials.gov/study/NCT03504579.

Implementing multiblock techniques in a full-scale plant scenario: On-line prediction of quality parameters in a continuous process for different acrylonitrile butadiene styrene (ABS) products.

BACKGROUND: The study explores the challenges of handling multiblock data of different natures (process and NIR sensors) for on-line quality prediction in a full-scale plant scenario, namely a plant operating in continuous on an industrial scale and producing different grade Acrylonitrile Butadiene Styrene (ABS) products. This environment is an ideal scenario to evaluate the use of multiblock data analysis methods, which can enhance data interpretation, visualization, and predictive performances. In particular, a novel multiblock extension of Locally Weighted PLS has been proposed by the authors, namely Locally Weighted Multiblock Partial Least Squares (LW-MB-PLS). Response-Oriented Sequential Alternation (ROSA) has also been employed to evaluate the diverse block relevance for the prediction of two quality parameters associated with the polymer. Data are split in blocks both according to sensor type and different plant sections, and different models have been built by incremental addition of data blocks to evaluate if early estimation of product quality is feasible. RESULTS: ROSA method showed promising predictive performance for both quality parameters, highlighting the most influential plant sections through the selection of data blocks. The results suggested that both early and late-stage sensors play crucial roles in predicting product quality. A reasonable estimation of quality parameters before production completion has been achieved. On the other hand, the proposed LW-MB-PLS, while comparable in predictive performances, allowed reducing systematic prediction errors for specific products. SIGNIFICANCE: This study contributes valuable insights for continuous production processes, aiding plant operators and paving the way for advancements in online quality prediction and control. Furthermore, it is implemented as a locally weighted extension of MB-PLS.

Microneedle electrochemical sensor based on disposable stainless-steel wire for real-time analysis of indole-3-acetic acid and salicylic acid in tomato leaves infected by Pst DC3000 in situ.

BACKGROUND: Indole-3-acetic acid (IAA) and salicylic acid (SA), pivotal regulators in plant growth, are integral to a variety of plant physiological activities. The ongoing and simultaneous monitoring of these hormones in vivo enhances our comprehension of their interactive and regulatory roles. Traditional detection methods, such as liquid chromatography-mass spectrometry, cannot obtain precise and immediate information on IAA and SA due to the complexity of sample processing. In contrast, the electrochemical detection method offers high sensitivity, rapid response times, and compactness, making it well-suited for in vivo or real-time detection applications. RESULTS: A microneedle electrochemical sensor system crafted from disposable stainless steel (SS) wire was specifically designed for the real-time assessment of IAA and SA in plant in situ. This sensor system included a SS wire (100 µm diameter) coated with carbon cement and multi-walled carbon nanotubes, a plain platinum wire (100 µm diameter), and an Ag/AgCl wire (100 µm diameter). Differential pulse voltammetry and amperometry were adopted for detecting SA and IAA within the range of 0.1-20 µM, respectively. This sensor was applied to track IAA and SA fluctuations in tomato leaves during PstDC3000 infection, offering continuous data. Observations indicated an uptick in SA levels following infection, while IAA production was suppressed. The newly developed disposable SS wire-based microneedle electrochemical sensor system is economical, suitable for mass production, and inflicts minimal damage during the monitoring of SA and IAA in plant tissues. SIGNIFICANCE: This disposable microneedle electrochemical sensor facilitates in vivo detection of IAA and SA in smaller plant tissues and allows for long-time monitoring of their concentrations, which not only propels research into the regulatory and interaction mechanisms of IAA and SA but also furnishes essential tools for advancing precision agriculture.

An Energy Efficient and Temperature Stable Digital FLL-based Wakeup Timer with Time-Domain Temperature Compensation.

This brief presents an on-chip digital intensive frequency-locked loop (DFLL)-based wakeup timer with a time-domain temperature compensation featuring a embedded temperature sensor. The proposed compensation exploits the deterministic temperature characteristics of two complementary resistors to stabilize the timer's operating frequency across the temperature by modulating the activation time window of the two resistors. As a result, it achieves a fine trimming step (± 1 ppm), allowing a small frequency error after trimming (<± 20 ppm). By reusing the DFLL structure, instead of employing a dedicated sensor, the temperature sensing operates in the background with negligible power (2 %) and hardware overhead (< 1 %). The chip is fabricated in 40 nm CMOS, resulting in 0.9 pJ/cycle energy efficiency while achieving 8 ppm/ºC from -40ºC to 80ºC.

Proof of concept: real-time viability and metabolic profiling of probiotics with isothermal microcalorimetry.

Isothermal microcalorimetry (IMC) is a potent analytical method for the real-time assessment of microbial metabolic activity, which serves as an indicator of microbial viability. This approach is highly relevant to the fields of probiotics and Live Biotherapeutic Products (LBPs), offering insights into microbial viability and growth kinetics. One important characteristic of IMC is its ability to measure microbial metabolic activity separately from cellular enumeration. This is particularly useful in situations where continuous tracking of bacterial activity is challenging. The focus on metabolic activity significantly benefits both probiotic research and industrial microbiology applications. IMC's versatility in handling different media matrices allows for the implementation of viability assessments under conditions that mirror those found in various industrial environments or biological models. In our study, we provide a proof of concept for the application of IMC in determining viability and growth dynamics and their correlation with bacterial count in probiotic organisms. Our findings reinforce the potential of IMC as a key method for process enhancement and accurate strain characterization within the probiotic sector. This supports the broader objective of refining the systematic approach and methods used during the development process, thereby providing detailed insights into probiotics and LBPs.

Adaptive EMG decomposition in dynamic conditions based on online learning metrics with tunable hyperparameters.

OBJECTIVE: Robustness to non-stationary conditions is essential to develop stable and accurate wearable neural interfaces. APPROACH: We propose a novel adaptive electromyography (EMG) decomposition algorithm that builds on blind source separation methods by leveraging the Kullback-Liebler divergence and kurtosis of the signals as metrics for online learning. The proposed approach provides a theoretical framework to tune the adaptation hyperparameters and compensate for non-stationarities in the mixing matrix, such as due to dynamic contractions, and to identify the underlying motor neuron (MN) discharges. The adaptation is performed in real-time (~22 ms of computational time per 100-ms batches). MAIN RESULTS: The proposed adaptation algorithm significantly improved all decomposition performance metrics with respect to the absence of adaptation in a wide range of motion of the wrist (80°). The rate of agreement, sensitivity, and precision were ≥ 90% in ≥ 80% of the cases in both simulated and experimentally recorded data, according to a two- source validation approach. SIGNIFICANCE: The findings demonstrate the feasibility of accurately decoding MN discharges in real-time during dynamic contractions from wearable systems mounted at the wrist and forearm. Moreover, the study proposes an experimental validation method for EMG decomposition in dynamic tasks.

Real-time imaging of intracellular deformation dynamics in vibrated adherent cell cultures.

Mechanical vibration has been shown to regulate cell proliferation and differentiation in vitro and in vivo. However, the mechanism of its cellular mechanotransduction remains unclear. Although the measurement of intracellular deformation dynamics under mechanical vibration could reveal more detailed mechanisms, corroborating experimental evidence is lacking due to technical difficulties. In this study, we aimed to propose a real-time imaging method of intracellular structure deformation dynamics in vibrated adherent cell cultures and investigate whether organelles such as actin filaments connected to a nucleus and the nucleus itself show deformation under horizontal mechanical vibration. The proposed real-time imaging was achieved by conducting vibration isolation and making design improvements to the experimental setup; using a high-speed and high-sensitivity camera with a global shutter; and reducing image blur using a stroboscope technique. Using our system, we successfully produced the first experimental report on the existence of the deformation of organelles connected to a nucleus and the nucleus itself under horizontal mechanical vibration. Furthermore, the intracellular deformation difference between HeLa and MC3T3-E1 cells measured under horizontal mechanical vibration agrees with the prediction of their intracellular structure based on the mechanical vibration theory. These results provide new findings about the cellular mechanotransduction mechanism under mechanical vibration.

Study on the relationship between Methylation of Neuregulin (NRG) Gene and Cervical Carcinoma.

Objective: To investigate the relationship between the DNA methylation state of NRG1 promoter and its expression changes, and to analyze the clinical significance of its regulatory mechanism of DNA methylation in cervical carcinoma. Methods: This was a retrospective study. One-hundred and twenty patients from the Department of Gynecology of Cangzhou People's Hospital from September 2017 to September 2019 were selected, including 40 cases of cervical SCC, 40 cases of high grade squamous intraepithelial lesions(HSIL) and 40 cases of control cervical tissues. RT-qPCR, immunohistochemistry and DNA methylation-specific PCR(MSP) were used to detect the mRNA and protein expression of NRG1 and DNA methylation status in different tissue types. Results: Immunohistochemical results showed that the positive protein expression rate of NRG1 gene in the SCC group was lower than that in both HSIL and Control groups. RT-qPCR results showed that the mRNA gene of NRG1 gradually decreased in expression with the increase of cervical tissue lesions, with a statistically significant difference. Similarly, it also found that the mRNA expression level of NRG1 in the SCC group was independent of patients' age (p>0.05), but significantly correlated with tumor pathological staging, surgical pathology staging and lymphatic metastasis (p<0.05). Furthermore, methylation-specific PCR results revealed a significantly higher DNA methylation rate of NRG1 gene in the SCC group than in both HSIL and Control groups, with a statistically significant difference. Moreover, the methylation degree of NRG1 gene in SCC tissues was negatively correlated with its mRNA expression (p<0.05). Conclusions: Abnormal DNA hypermethylation of NRG1 gene inhibits the expression of mRNA and protein in the progression of cervical tissue from normal to cancerous state, which is involved in the occurrence and development of cervical carcinoma.

Winter wheat ear counting based on improved YOLOv7x and Kalman filter tracking algorithm with video streaming.

Accurate and real-time field wheat ear counting is of great significance for wheat yield prediction, genetic breeding and optimized planting management. In order to realize wheat ear detection and counting under the large-resolution Unmanned Aerial Vehicle (UAV) video, Space to depth (SPD) module was added to the deep learning model YOLOv7x. The Normalized Gaussian Wasserstein Distance (NWD) Loss function is designed to create a new detection model YOLOv7xSPD. The precision, recall, F1 score and AP of the model on the test set are 95.85%, 94.71%, 95.28%, and 94.99%, respectively. The AP value is 1.67% higher than that of YOLOv7x, and 10.41%, 39.32%, 2.96%, and 0.22% higher than that of Faster RCNN, SSD, YOLOv5s, and YOLOv7. YOLOv7xSPD is combined with the Kalman filter tracking and the Hungarian matching algorithm to establish a wheat ear counting model with the video flow, called YOLOv7xSPD Counter, which can realize real-time counting of wheat ears in the field. In the video with a resolution of 3840×2160, the detection frame rate of YOLOv7xSPD Counter is about 5.5FPS. The counting results are highly correlated with the ground truth number (R2 = 0.99), and can provide model basis for wheat yield prediction, genetic breeding and optimized planting management.

Lessons learned: avoiding bias via multi-state analysis of patients' trajectories in real-time.

Objectives: Many studies have attempted to determine the disease severity and patterns of COVID-19. However, at the beginning of the pandemic, the complex patients' trajectories were only descriptively reported, and many analyses were worryingly prone to time-dependent-, selection-, and competing risk biases. Multi-state models avoid these biases by jointly analysing multiple clinical outcomes while taking into account their time dependency, including current cases, and modelling competing events. This paper uses a publicly available data set from the first wave in Israel as an example to demonstrate the benefits of analysing hospital data via multi-state methodology. Methods: We compared the outcome of the data analysis using multi-state models with the outcome obtained when various forms of bias are ignored. Furthermore, we used Cox regression to model the transitions among the states in a multi-state model. This allowed for the comparison of the covariates' influence on transition rates between the two states. Lastly, we calculated expected lengths of stay and state probabilities based on the multi-state model and visualised it using stacked probability plots. Results: Compared to standard methods, multi-state models avoid many biases in the analysis of real-time disease developments. The utility of multi-state models is further highlighted through the use of stacked probability plots, which visualise the results. In addition, by stratification of disease patterns by subgroups and visualisation of the distribution of possible outcomes, these models bring the data into an interpretable form. Conclusion: To accurately guide the provision of medical resources, this paper recommends the real-time collection of hospital data and its analysis using multi-state models, as this method eliminates many potential biases. By applying multi-state models to real-time data, the gained knowledge allows rapid detection of altered disease courses when new variants arise, which is essential when informing medical and political decision-makers as well as the general population.

Suppressing the HIFU interference in ultrasound guiding images with a diffusion-based deep learning model.

BACKGROUND AND OBJECTIVES: In ultrasound guided high-intensity focused ultrasound (HIFU) surgery, it is necessary to transmit sound waves at different frequencies simultaneously using two transducers: one for the HIFU therapy and another for the ultrasound imaging guidance. In this specific setting, real-time monitoring of non-invasive surgery is challenging due to severe contamination of the ultrasound guiding images by strong acoustic interference from the HIFU sonication. METHODS: This paper proposed the use of a deep learning (DL) solution, specifically a diffusion implicit model, to suppress the HIFU interference. We considered the images contaminated with HIFU interference as low-resolution images, and those free from interference as high-resolution. While suppressing HIFU interference using the diffusion implicit (HIFU-Diff) model, the task was transformed into generating a high-resolution image through a series of forward diffusion steps and reverse sampling. A series of ex-vivo and in-vivo experiments, conducted under various parameters, were designed to validate the performance of the proposed network. RESULTS: Quantitative evaluation and statistical analysis demonstrated that the HIFU-Diff network achieved superior performance in reconstructing interference-free images under a variety of ex-vivo and in-vivo conditions, compared to the most commonly used notch filtering and the recent 1D FUS-Net deep learning network. The HIFU-Diff maintains high performance with 'unseen' datasets from separate experiments, and its superiority is more pronounced under strong HIFU interferences and in complex in-vivo situations. Furthermore, the reconstructed interference-free images can also be used for quantitative attenuation imaging, indicating that the network preserves acoustic characteristics of the ultrasound images. CONCLUSIONS: With the proposed technique, HIFU therapy and the ultrasound imaging can be conducted simultaneously, allowing for real-time monitoring of the treatment process. This capability could significantly enhance the safety and efficacy of the non-invasive treatment across various clinical applications. To the best of our knowledge, this is the first diffusion-based model developed for HIFU interference suppression.

In vivo detection of Alzheimer's and Lewy body disease concurrence: Clinical implications and future perspectives.

INTRODUCTION: The recent introduction of seed amplification assays (SAAs) detecting misfolded α-synuclein, a pathology-specific marker for Lewy body disease (LBD), has allowed the in vivo identification and phenotypic characterization of patients with co-occurring Alzheimer's disease (AD) and LBD since the early clinical or even preclinical stage. METHODS: We reviewed studies with an in vivo biomarker-based diagnosis of AD-LBD copathology. RESULTS: Studies in large cohorts of cognitively impaired individuals have shown that cerebrospinal fluid (CSF) biomarkers detect the coexistence of AD and LB pathology in approximately 20%-25% of them, independently of the primary clinical diagnosis. Compared to those with pure AD, AD-LBD patients showed worse global cognition, especially in attentive/executive and visuospatial functions, and worse motor functions. In cognitively unimpaired individuals, concurrent AD-LBD pathologies predicted longitudinal cognitive progression with faster worsening of global cognition, memory, and attentive/executive functions. DISCUSSION: Future research studies aiming for a better precision medicine approach should develop SAAs further to reach a quantitative evaluation or staging of each underlying pathology using a single biofluid sample. HIGHLIGHTS: α-Synuclein seed amplification assays (SAAs) provide a specific marker for Lewy body disease (LBD). SAAs allow for the in vivo identification of co-occurring LBD in patients with Alzheimer's disease (AD). AD-LBD coexist in 20-25% of cognitively impaired elderly individuals, and ∼8% of those asymptomatic. Compared to pure AD, AD-LBD causes a faster worsening of cognitive functions. AD-LBD is associated with worse attentive/executive, memory, visuospatial and motor functions.

Prognostic value of oxidative phosphorylation-related genes in hepatocellular carcinoma.

PURPOSE: Hepatocellular carcinoma (HCC) is the most prevalent malignancies worldwide. Recently, oxidative phosphorylation (OXPHOS) has received extensive concern as an emerging target in antitumor therapy. However, the OXPHOS-involved underlying genes and clinical utilization in HCC remain worth exploring. The present research aimed to create an OXPHOS-relevant signature in HCC. PATIENTS AND METHODS: In this study, the prognostic signature genes linked with OXPHOS were identified, and prognostic models were built using least absolute shrinkage and selection operator (LASSO) cox regression analysis. Furthermore, the combination study of immune microenvironment and signature genes looked into the involvement of immune cells in signature-based genes in HCC. Following that, chemotherapeutic drug sensitivity and immunotherapy analysis was implemented to predict clinical efficacy in HCC patients. Finally, clinical samples were collected to measure the expression of OXPHOS-related signature genes. RESULTS: Following a series of screens, six prognostic signature genes related with OXPHOS were identified: MRPS23, MPV17, MAPK3, IGF2BP2, CDK5, and IDH2, on which a risk model was built. The findings revealed a significant drop in the survival rate of HCC patients as their risk score increased. Meanwhile, independent prognostic study demonstrated that the risk score could accurately identify HCC patients. Immuno-microenvironmental correlation research suggested that the prognostic characteristics could serve as a reference index for both immunotherapy and chemotherapy. Finally, RT-qPCR exhibited a trend in signature gene expression that was consistent with the results. CONCLUSION: In this study, a total of six prognostic genes associated with OXPHOS were selected and a prognostic model was constructed, providing an essential reference for the study of OXPHOS in HCC.

Brain-machine interactive neuromodulation research tool with edge AI computing.

Closed-loop neuromodulation with intelligence methods has shown great potentials in providing novel neuro-technology for treating neurological and psychiatric diseases. Development of brain-machine interactive neuromodulation strategies could lead to breakthroughs in precision and personalized electronic medicine. The neuromodulation research tool integrating artificial intelligent computing and performing neural sensing and stimulation in real-time could accelerate the development of closed-loop neuromodulation strategies and translational research into clinical application. In this study, we developed a brain-machine interactive neuromodulation research tool (BMINT), which has capabilities of neurophysiological signals sensing, computing with mainstream machine learning algorithms and delivering electrical stimulation pulse by pulse in real-time. The BMINT research tool achieved system time delay under 3 ms, and computing capabilities in feasible computation cost, efficient deployment of machine learning algorithms and acceleration process. Intelligent computing framework embedded in the BMINT enable real-time closed-loop neuromodulation developed with mainstream AI ecosystem resources. The BMINT could provide timely contribution to accelerate the translational research of intelligent neuromodulation by integrating neural sensing, edge AI computing and stimulation with AI ecosystems.

Quadruplex qPCR for detection and discrimination of C. Coli,C. fetus, and C. Jejuni from other Campylobacter species in chicken and sheep meat.

Campylobacter is gram-negative bacteria considered the predominant genera isolated from poultry samples and associated with gastroenteritis. Due to the problems in conventional cultural methods of time-consuming and technically demanding requirements, a rapid and feasible method for their identification and discrimination of the closely related spp. Including Campylobacter coli, Campylobacter fetus, and Campylobacter jejuni is needed. This study analyzes the chicken and sheep meats samples (n = 125) using culture and pre-enrichment-based Quadraplex real-time PCR by targeting OrfA, CstA, HipO, and 16 S rRNA genes of C. coli, C. fetus, C. jejuni and Campylobacter spp. Respectively. The analysis of 125 chicken and sheep meat samples by culture and real-time PCR showed high concordance between the results of the two methods. The present study show high prevalence of Campylobacter species (35% and 32% from chicken and meat respectively) of which C. jejuni were the most abundant. Reaction efficiencies were between 90 and 110%, and detect as low as 8.9 fg in C. jejuni. The need for quick detection and discrimination methods in sheep and chicken meat can be met using the described Quadraplex real-time PCR methodology.

Advancing COVID-19 Diagnosis: Enhancement in SERS-PCR with 30-nm Au Nanoparticle-Internalized Nanodimpled Substrates.

Real-time polymerase chain reaction (RT-PCR) with fluorescence detection is the gold standard for diagnosing coronavirus disease 2019 (COVID-19) However, the fluorescence detection in RT-PCR requires multiple amplification steps when the initial deoxyribonucleic acid (DNA) concentration is low. Therefore, this study has developed a highly sensitive surface-enhanced Raman scattering-based PCR (SERS-PCR) assay platform using the gold nanoparticle (AuNP)-internalized gold nanodimpled substrate (AuNDS) plasmonic platform. By comparing different sizes of AuNPs, it is observed that using 30 nm AuNPs improves the detection limit by approximately ten times compared to 70 nm AuNPs. Finite-difference time-domain (FDTD) simulations show that multiple hotspots are formed between AuNPs and the cavity surface and between AuNPs when 30 nm AuNPs are internalized in the cavity, generating a strong electric field. With this 30 nm AuNPs-AuNDS SERS platform, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) ribonucleic acid (RNA)-dependent RNA polymerase (RdRp) can be detected in only six amplification cycles, significantly improving over the 25 cycles required for RT-PCR. These findings pave the way for an amplification-free molecular diagnostic system based on SERS.

Innovative use of Australian cancer registry data for early detection of the effects of epidemics and other mass disruptions on cancer incidence.

BACKGROUND: Predictive modelling using pre-epidemic data have long been used to guide public health responses to communicable disease outbreaks and other health disruptions. In this study, cancer registry and related health data available 2-3 months from diagnosis were used to predict changes in cancer detection that otherwise would not have been identified until full registry processing was completed about 18-24 months later. A key question was whether these earlier data could be used to predict cancer incidence ahead of full processing by the cancer registry as a guide to more timely health responses. The setting was the Australian State of New South Wales, covering 31 % of the Australian population. The study year was 2020, the year of emergence of the COVID-19 pandemic. METHODS: Cancer detection in 2020 was modelled using data available 2-3 months after diagnosis. This was compared with data from full registry processing available from 2022. Data from pre-pandemic 2018 were used for exploratory model building. Models were tested using pre-pandemic 2019 data. Candidate predictor variables included pathology, surgery and radiation therapy reports, numbers of breast screens, colonoscopies, PSA tests, and melanoma excisions recorded by the universal Medical Benefits Schedule (MBS). Data were analysed for all cancers collectively and 5 leading types. RESULTS: Compared with full registry processing, modelled data for 2020 had a >95 % accuracy overall, indicating key points of inflexion of cancer detection over the COVID-disrupted 2020 period. These findings highlight the potential of predictive modelling of cancer-related data soon after diagnosis to reveal changes in cancer detection during epidemics and other health disruptions. CONCLUSIONS: Data available 2-3 months from diagnosis in the pandemic year indicated changes in cancer detection that were ultimately confirmed by fully-processed cancer registry data about 24 months later. This indicates the potential utility of using these early data in an early-warning system.

Continuous oxygen monitoring to enhance ex-vivo organ machine perfusion and reconstructive surgery.

Continuous oxygenation monitoring of machine-perfused organs or transposed autologous tissue is not currently implemented in clinical practice. Oxygenation is a critical parameter that could be used to verify tissue viability and guide corrective interventions, such as perfusion machine parameters or surgical revision. This work presents an innovative technology based on oxygen-sensitive, phosphorescent metalloporphyrin allowing continuous and non-invasive oxygen monitoring of ex-vivo perfused vascularized fasciocutaneous flaps. The method comprises a small, low-energy optical transcutaneous oxygen sensor applied on the flap's skin paddle as well as oxygen sensing devices placed into the tubing. An intermittent perfusion setting was designed to study the response time and accuracy of this technology over a total of 54 perfusion cycles. We further evaluated correlation between the continuous oxygen measurements and gold-standard perfusion viability metrics such as vascular resistance, with good agreement suggesting potential to monitor graft viability at high frequency, opening the possibility to employ feedback control algorithms in the future. This proof-of-concept study opens a range of research and clinical applications in reconstructive surgery and transplantation at a time when perfusion machines undergo rapid clinical adoption with potential to improve outcomes across a variety of surgical procedures and dramatically increase access to transplant medicine.

Metabolic profiling of single cells by exploiting NADH and FAD fluorescence via flow cytometry.

The metabolism of different cells within the same microenvironment can differ and dictate physiological or pathological adaptions. Current single-cell analysis methods of metabolism are not label-free. The study introduces a label-free, live-cell analysis method assessing endogenous fluorescence of NAD(P)H and FAD in surface-stained cells by flow cytometry. OxPhos inhibition, mitochondrial uncoupling, glucose exposure, genetic inactivation of glucose uptake and mitochondrial respiration alter the optical redox ratios of FAD and NAD(P)H as measured by flow cytometry. Those alterations correlate strongly with measurements obtained by extracellular flux analysis. Consequently, metabolically distinct live B-cell populations can be resolved, showing that human memory B-cells from peripheral blood exhibit a higher glycolytic flexibility than naïve B cells. Moreover, the comparison of blood-derived B- and T-lymphocytes from healthy donors and rheumatoid arthritis patients unleashes rheumatoid arthritis-associated metabolic traits in human naïve and memory B-lymphocytes. Taken together, these data show that the optical redox ratio can depict metabolic differences in distinct cell populations by flow cytometry.

Molecular and serological prevalence of Leptospira spp. among slaughtered cattle and associated risk factors in the Bahr El Ghazal region of South Sudan.

INTRODUCTION: Leptospirosis is a neglected emerging and zoonotic disease reported worldwide. This study sought to determine the molecular and serological prevalence of Leptospira spp. and the associated risk factors in slaughtered cattle from the Bahr El Ghazal region of South Sudan. MATERIALS AND METHODS: Between January 16th and February 25th, 2023, blood and urine samples were collected from 402 cattle at the Lokoloko Municipal Slaughterhouse in Western Bahr El-Ghazal State. Serum samples were tested using the microscopic agglutination test (MAT), with a panel of 12 serovars (sv) from 12 serogroups (sg) and 4 species (spp) of Leptospira spp. These serovars had been previously identified in Sudan and the East African region. Simultaneously, 400 corresponding urine samples were screened using qualitative real-time polymerase chain reaction (PCR) to detect the shedding of Leptospira spp. in urine. To identify the associated risk factors, the age, sex, breed and body condition score of each sampled cattle was noted at the time of sampling and subsequently analysed using logistic regression models. RESULTS: Among the 402 serum samples screened, a substantial 81.8% (329/402, 95% CI 77.9-85.3) displayed seropositivity for Leptospira spp. with a MAT titre ≥ 100. The prevalence of urine shedding determined by PCR was 6% (23/400, 95% CI 3.8-8.4), while probable recent leptospirosis with a MAT ≥ 1:800 was observed in 33.1% (133/402, 95% CI 28.6-37.8) of the cattle. Multiple reactions were detected in 34.8% (140/402, 95% CI 30.6-39.5) serum samples. The seropositivity was against L. borgpetersenii sg. Tarassovi (78.6%; 316/402, 95% CI 74.4-82.3), followed by L. borgpetersenii sg. Ballum at 20.4% (82/402, 95% CI, 16.7-24.4%), L. kirschneri sg. Autumnalis At 8.7% (35/402, 95% CI 5.7-11.7), L. interrogans sg. of Pomona at 7.0% (28/402, 95% CI 4.5-9.5), and L. interrogans sg. Hebdomadis was 5.0% (20/402, 95% CI 2.8-7.2). Several risk factors are associated with seropositivity. Older animals (≥ 2 years) had 2.0 times greater odds (95% CI 1.14-3.5) of being seropositive than younger animals (< 2 years), P-value = 0.016. Female animals demonstrated 2.1 times greater odds (95% CI 1.2-3.6) of seropositivity than males did (P-value = 0.008). Additionally, Felata/Mbororo cattle exhibited 2.4 times greater odds (95% CI 1.3-4.5) of being seropositive than did local Nilotic cattle (P-value = 0.005). The agreement between the MAT and PCR results was poor, as indicated by a kappa statistic value of 0.001 and a P-value of 0.913. But there was a moderate agreement between MAT high titres ≥ 800 and PCR positivity with a kappa statistic value = 0.501 and a P-value < 0.001. CONCLUSION: In addition to the high seroprevalence, Leptospira spp. were found in the urine of slaughtered cattle, suggesting that leptospirosis is endemic to the study area. This finding underscores the significance of cattle as potential sources of infection for slaughterhouse workers, the general public, and other animal species. To address this issue effectively in the Bahr El Ghazal Region and South Sudan, a comprehensive strategy involving a multidisciplinary approach is essential to minimize disease among animals, hence reducing potential zoonotic risks to humans.