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Urinary tract infections (UTIs) are among the most common bacterial infections worldwide and are particularly prevalent in women. Recurrent UTIs significantly diminish quality of life due to their symptoms and frequent relapses. Patients often experience immediate relapse following slightly strenuous activities or intense psychological stress. In this review, we explore why infections persist despite the advent of various treatments and suggest strategies to manage recurrent cystitis by targeting the mechanisms of adhesion and infection. Vitamin D levels and the expression of neutrophil gelatinase-associated lipocalin are linked to the recurrence of UTIs. During a UTI, bacteria employ adhesins to invade the urinary tract, adhere to urothelial cells, and then penetrate these cells, where they rapidly multiply to establish intracellular bacterial communities. Bacteria can also form quiescent intracellular reservoirs that escape immune responses and antibiotic treatments, leading to recurrence under certain conditions. The surface proteins of bacteria and D-mannose are crucial in the adhesion of bacteria to the urothelium. Understanding these processes provides valuable insights into potential therapeutic approaches that focus on preventing bacterial attachment and cluster formation. By disrupting the ability of bacteria to adhere to and form clusters on cells, we can better manage recurrent UTIs and improve patient outcomes.

Multilabel remote sensing image classification is a fundamental task that classifies multiple objects and land covers within an image. However, training deep learning models for this task requires a considerable cost of labeling. While several efforts to reduce labeling costs have been made, they often result in decreased label quality and the inclusion of incorrect (i.e., noisy) labels. To tackle this issue, algorithms for training deep learning models robust to multilabel noise have been proposed in the literature. Nonetheless, the efficacy of these algorithms has been evaluated only under instance-independent multilabel noise, where noise is generated regardless of the individual characteristics and features of each remote sensing image. In this article, we introduce generating instance-dependent multilabel noise into multilabel remote sensing image datasets for the first time. We leverage a vision-language model with zero-shot prediction capabilities to compute categorywise prediction scores for each image, based on which we generate multilabel noise in an instance-dependent manner. We demonstrate that the proposed instance-dependent multilabel noise is more feasibly generated with respect to individual images compared to traditional instance-independent multilabel noise. We also demonstrate that a more challenging noise scenario is generated, which leads to a more complex decision boundary and stronger overfitting during deep learning model training. Finally, we re-evaluate existing noise-robust training algorithms under the generated instance-dependent multilabel noise and observe that several algorithms exhibit limited robustness against instance-dependent multilabel noise.

Purpose The development of optics-based wearables for bladder volume monitoring has emerged as a significant topic in recent years. Given the innovative nature of this technology, there is currently no bladder phantom available to effectively validate these devices against more established gold standards, such as ultrasound. In this study, we showcase and demonstrate the performance of our hybrid bladder phantom by using an optical device and making comparisons with ultrasound. Methods A series of validation tests, including phantom repeatability, ultrasound scanning, and an optical test, were performed. A near-infrared optical device was utilized to conduct diffuse optical spectroscopy (DOS). Machine learning models were employed to construct predictive models of volume using optical signals. Results The size and position of an embedded balloon, serving as an analog for the bladder, were shown to be consistent when infused with 100 mL to 350 mL of water during repeatability testing. For DOS data, we present 7 types of machine learningbased models based on different optical signals. The 2 best-performing models demonstrated an average absolute volume error ranging from 12.7 mL to 19.0 mL. Conclusions In this study, we introduced a hybrid bladder phantom designed for the validation of near-infrared spectroscopy-based bladder monitoring devices in comparison with ultrasound techniques. By offering a reproducible and robust validation tool, we aim to support the advancement of next-generation optical wearables for bladder volume monitoring.

Although granule cell dispersion (GCD) in the hippocampus is known to be an important feature associated with epileptic seizures in temporal lobe epilepsy (TLE), the endogenous molecules that regulate GCD are largely unknown. In the present study, we have examined whether there is any change in AEG-1 expression in the hippocampus of a kainic acid (KA)-induced mouse model of TLE. In addition, we have investigated whether the modulation of astrocyte elevated gene-1 (AEG-1) expression in the dentate gyrus (DG) by intracranial injection of adeno-associated virus 1 (AAV1) influences pathological phenotypes such as GCD formation and seizure susceptibility in a KA-treated mouse. We have identified that the protein expression of AEG-1 is upregulated in the DG of a KA-induced mouse model of TLE. We further demonstrated that AEG-1 upregulation by AAV1 delivery in the DG-induced anticonvulsant activities such as the delay of seizure onset and inhibition of spontaneous recurrent seizures (SRS) through GCD suppression in the mouse model of TLE, while the inhibition of AEG-1 expression increased susceptibility to seizures. The present observations suggest that AEG-1 is a potent regulator of GCD formation and seizure development associated with TLE, and the significant induction of AEG-1 in the DG may have therapeutic potential against epilepsy.

IntroductionMild cognitive impairment (MCI) is often described as an intermediate stage of the normal cognitive decline associated with aging and dementia. There is a growing interest in various non-pharmacological interventions for MCI to delay the onset and inhibit the progressive deterioration of daily life functions. Previous studies suggest that cognitive training (CT) contributes to the restoration of working memory and that the brain-computer-interface technique can be applied to elicit a more effective treatment response. However, these techniques have certain limitations. Thus, in this preliminary study, we applied the neurofeedback paradigm during CT to increase the working memory function of patients with MCI.MethodsNear-infrared spectroscopy (NIRS) was used to provide neurofeedback by measuring the changes in oxygenated hemoglobin in the prefrontal cortex. Thirteen elderly MCI patients who received CT-neurofeedback sessions four times on the left dorsolateral prefrontal cortex (dlPFC) once a week were recruited as participants.ResultsCompared with pre-intervention, the activity of the targeted brain region increased when the participants first engaged in the training; after 4 weeks of training, oxygen saturation was significantly decreased in the left dlPFC. The participants demonstrated significantly improved working memory compared with pre-intervention and decreased activity significantly correlated with improved cognitive performance.ConclusionOur results suggest that the applications for evaluating brain-computer interfaces can aid in elucidation of the subjective mental workload that may create additional or decreased task workloads due to CT.

Purpose Current guidelines recommend clean intermittent catheterization (CIC) at regular time intervals for patients with spinal cord injuries; however, many patients experience difficulties. Performing time-based CIC outside the home is a significant burden for patients. In this study, we aimed to overcome the limitations of the current guidelines by developing a digital device to monitor bladder urine volume in real-time. Methods The optode sensor is a near-infrared spectroscopy (NIRS)-based wearable device intended to be attached to the skin of the lower abdomen where the bladder is located. The sensor’s primary function is to detect changes in urine volume within the bladder. An in vitro study was conducted using a bladder phantom that mimicked the optical properties of the lower abdomen. To validate the data in the human body at the proof-of-concept level, one volunteer attached the device to the lower abdomen to measure the light intensity between the first voiding and immediately before the second voiding. Results The degree of attenuation at the maximum test volume was equivalent across experiments, and the optode sensor with multiplex measurements demonstrated robust performance for patient diversity. Moreover, the symmetric feature of the matrix was deemed a potential parameter for identifying the accuracy of sensor localization in a deep-learning model. The validated feasibility of the sensor showed almost the same results as an ultrasound scanner, which is routinely used in the clinical field. Conclusions The optode sensor of the NIRS-based wearable device can measure the urine volume in the bladder in real-time.

The efficient design optimization of electric machines for electric power steering (EPS) applications poses challenges in meeting demanding performance criteria, including high power density, efficiency, and low vibration. Traditional optimization approaches often fail to find a global solution or suffer from excessive computation time. In response to the limitations of traditional approaches, this paper introduces a novel methodology by incorporating a Gaussian process-based adaptive sampling technique into a surrogate-assisted optimization process using a metaheuristic algorithm. Validation on a 72-slot/8-pole interior permanent magnet (IPM) machine demonstrates the superiority of the proposed approach, showcasing improved exploitation–exploration balance, faster convergence, and enhanced repeatability compared to conventional optimization methods. The proposed design process is then applied to two surface PM (SPM) machine configurations with 9-slot/6-pole and 12-slot/10-pole combinations for EPS applications. The results indicate that the 12-slot/10-pole SPM design surpasses the alternative design in torque density, efficiency, cogging torque, torque ripple, and manufacturability.

We recently demonstrated that prothrombin kringle-2 (pKr-2) derived from blood-brain barrier (BBB) disruption could induce hippocampal neurodegeneration and object recognition impairment through neurotoxic inflammatory responses in the five familial Alzheimer's disease mutation (5XFAD) mice. In the present study, we aimed to determine whether pKr-2 induces microglial activation by stimulating toll-like receptor 4 (TLR4) upregulation and examine whether this response contributes to pKr-2-induced neuroinflammatory damage in the hippocampi of mice models. We observed that inflammatory responses induced by pKr-2 administration in the hippocampi of wild-type mice were significantly abrogated in TLR4-deficient mice (TLR4−/−), and caffeine supply or rivaroxaban treatment that inhibits the overexpression of hippocampal pKr-2 reduced TLR4 upregulation in 5XFAD mice, resulting in the inhibition of neuroinflammatory responses. Similar to the expression patterns of pKr-2, TLR4, and the TLR4 transcription factors, PU.1 and p-c-Jun, seen in the postmortem hippocampal tissues of Alzheimer's disease (AD) patients, our results additionally showed the influence of transcriptional regulation on TLR4 expression following pKr-2 expression in triggering the production of neurotoxic inflammatory mediators. Therefore, we conclude that pKr-2 may play a role in initiating upregulation of microglial TLR4, consequently inducing hippocampal neurodegeneration. Furthermore, the control of pKr-2-induced microglial TLR4 could be a useful therapeutic strategy against hippocampal neurodegeneration in AD.

Abstract To evaluate the efficacy of low-level light therapy (LLLT) with near-infrared light-emitting diodes (LED-LLLT) for the treatment of dry eye. 40 patients were randomly assigned with a 1:1 allocation ratio to receive LED-LLLT (LLLT group, n = 20) or placebo treatment (placebo group, n = 20). Patients in the LLLT group received LLLT twice a week for 3 weeks, for a total of 6 treatment sessions. The primary endpoint was the changes in the fluorescein corneal staining (FCS) score. The secondary endpoints were the changes in the ocular surface disease index (OSDI) score, lissamine green conjunctival staining (LGCS) scores, tear film break-up time (TBUT), Schirmer test, and the meibomian gland dysfunction (MGD) index. These were evaluated before treatment and 4 weeks after start of treatment. The mean difference of score change in primary endpoint revealed significant improvement in the LLLT group, compared to the placebo. Among secondary endpoints, LGCS, Schirmer's test, upper meibography scores showed significant improvements, while TBUT, lid debris, lid swelling, lid telangiectasia, meibomian gland secretion and expressibility scores had slight improvement without significant differences. No serious adverse events were observed. The use of LED-LLLT for the treatment of dry eye and MGD appears to be safe and beneficial.

The increasing complexity of mathematical models developed as part of the recent advancements in autonomous mobility platforms has led to an escalation in uncertainty. Despite the intricate nature of such models, the detection, decision, and control methods for autonomous mobility path tracking remain critical. This study aims to achieve path tracking based on pixel-based control errors without parameters in the mathematical model. The proposed approach entails deriving control errors from a multi-particle filter based on a camera, estimating the error dynamics coefficients through a recursive least squares (RLS) approach, and using the sliding mode approach and weighted injection to formulate a cost function that leverages the estimated coefficients and control errors. The resultant adaptive steering control expedites the convergence of control errors towards zero by determining the magnitude of the injection variable based on the control errors and the finite-time convergence condition. The efficacy of the proposed approach is evaluated through an S-curved and elliptical path using autonomous mobility equipped with a single steering and driving module. The results demonstrate the capability of the approach to reasonably track target paths through driving and steering control facilitated by a multi-particle filter and a lidar-based obstacle detection system.

Urinary tract infections (UTIs) are the most common infectious disease and are mainly caused by Escherichia coli. In this review, we introduce the current concept of recurrent UTI (rUTI) based on recent research dealing with pathophysiology of the disease. Although urine is considered sterile, recent studies dealing with microbiome have proposed different ideas. UTIs have typically been considered as extracellular infections, but recently, uropathogenic Escherichia coli (UPEC) has been shown to bind and replicate in the urothelium to make intracellular bacterial communities. Binding UPECs might proceed in many ways including extracellular expulsion for clearance or survival and quiescent intracellular reservoirs that can cause rUTI. Moreover, it is also suggested that other important factors, such as lipopolysaccharide and multimicrobial infection, can be the cause of rUTI. This review article reveals a key mechanism of recurrence and discusses what makes a pathway of resolution or recurrence in a host after initial infection.

The blood–brain barrier (BBB) restricts entry of neurotoxic plasma components, blood cells, and pathogens into the brain, leading to proper neuronal functioning. BBB impairment leads to blood-borne protein infiltration such as prothrombin, thrombin, prothrombin kringle-2, fibrinogen, fibrin, and other harmful substances. Thus, microglial activation and release of pro-inflammatory mediators commence, resulting in neuronal damage and leading to impaired cognition via neuroinflammatory responses, which are important features observed in the brain of Alzheimer’s disease (AD) patients. Moreover, these blood-borne proteins cluster with the amyloid beta plaque in the brain, exacerbating microglial activation, neuroinflammation, tau phosphorylation, and oxidative stress. These mechanisms work in concert and reinforce each other, contributing to the typical pathological changes in AD in the brain. Therefore, the identification of blood-borne proteins and the mechanisms involved in microglial activation and neuroinflammatory damage can be a promising therapeutic strategy for AD prevention. In this article, we review the current knowledge regarding the mechanisms of microglial activation-mediated neuroinflammation caused by the influx of blood-borne proteins into the brain via BBB disruption. Subsequently, the mechanisms of drugs that inhibit blood-borne proteins, as a potential therapeutic approach for AD, along with the limitations and potential challenges of these approaches, are also summarized.

Abstract Cerebral microhemorrhages (CMHs) are associated with cerebrovascular disease, cognitive impairment, and normal aging. One method to study CMHs is to analyze histological sections (5–40 μm) stained with Prussian blue. Currently, users manually and subjectively identify and quantify Prussian blue-stained regions of interest, which is prone to inter-individual variability and can lead to significant delays in data analysis. To improve this labor-intensive process, we developed and compared three digital pathology approaches to identify and quantify CMHs from Prussian blue-stained brain sections: (1) ratiometric analysis of RGB pixel values, (2) phasor analysis of RGB images, and (3) deep learning using a mask region-based convolutional neural network. We applied these approaches to a preclinical mouse model of inflammation-induced CMHs. One-hundred CMHs were imaged using a 20 × objective and RGB color camera. To determine the ground truth, four users independently annotated Prussian blue-labeled CMHs. The deep learning and ratiometric approaches performed better than the phasor analysis approach compared to the ground truth. The deep learning approach had the most precision of the three methods. The ratiometric approach has the most versatility and maintained accuracy, albeit with less precision. Our data suggest that implementing these methods to analyze CMH images can drastically increase the processing speed while maintaining precision and accuracy.

Recently, in various fields, research into the path tracking of autonomous vehicles and automated guided vehicles has been conducted to improve worker safety, convenience, and work efficiency. For path tracking of various systems applied to autonomous driving technology, it is necessary to recognize the surrounding environment, determine technology accordingly, and develop control methods. Various sensors and artificial-intelligence-based perception methods have limitations in that they must learn a large amount of data. Therefore, a particle-filter-based path tracking algorithm using a monocular camera was used for the recognition of target RGB. The path tracking errors were calculated and a linear-quadratic-regulator-based desired steering angle were derived. The autonomous trucks were steered and driven using a pulse-width-modulation-based steering and driving motor. Based on an autonomous truck with a single steering and driving module, it was verified that the path tracking could be used in three evaluation scenarios. To compare the LQR-based path tracking control performance proposed in this paper, an elliptical path tracking scenario using a conventional sliding mode control with robust control performance was performed. The results show that the RMS of the lateral preview error of the SMC was approximately 18% larger than that of the LQR-based method.

This study investigated the therapeutic effects of transplanting human mesenchymal stem cells (hMSCs) into wild-type mice that were intraperitoneally administered cytosine arabinoside (Ara-C) to develop cerebellar ataxia (CA) during the first three postnatal days. hMSCs were intrathecally injected into 10-week-old mice once or thrice at 4-week intervals. Compared to the nontreated mice, the hMSC-treated mice showed improved motor and balance coordination, as measured using the rotarod, open-field, and ataxic scoring assessments, and increased protein levels in Purkinje and cerebellar granule cells, as measured using calbindin and NeuN protein markers. Multiple hMSC injections preserved Ara-C-induced cerebellar neuronal loss and improved cerebellar weight. Furthermore, the hMSC implantation significantly elevated the levels of neurotrophic factors, including brain-derived and glial cell line-derived neurotrophic factors, and suppressed TNF-α-, IL-1β-, and iNOS-mediated proinflammatory responses. Collectively, our results demonstrate that hMSCs exhibit therapeutic potential for Ara-C-induced CA by protecting neurons through the stimulation of neurotrophic factors and inhibition of cerebellar inflammatory responses, which can improve motor behavior and alleviate ataxia-related neuropathology. In summary, this study suggests that hMSC administration, particularly multiple treatments, can effectively treat ataxia-related symptoms with cerebellar toxicity.

Stress urinary incontinence (SUI) is a highly prevalent health condition that significantly impacts the quality of life. Traditional methods of treatment for SUI, such as pubovaginal sling and Burch colposuspension, have been replaced by the midurethral sling because of its high efficacy, low complication and morbidity rates, and short learning curve. Although multiple behavioral and operative treatments exist, midurethral slings are the gold standard for the treatment of SUI in women. However, several reports have raised concerns about complications caused by the synthetic mesh used in midurethral slings. Therefore, surgical treatment for SUI in women must be chosen with care, taking into account potential complications. Herein, we review the current safety issues pertaining to the use of meshes, the efficacy of traditional surgeries, old and new midurethral slings, and recent data comparing the efficacy and safety of different surgical options. This review is aimed at developing practical guidelines for choosing surgical options for women with SUI.

Taste bud cell differentiation is extremely important for taste sensation. Immature taste bud cells cannot function during taste perception transmission to the nerve. In this study, we investigated whether hedgehog signaling affected taste bud cell differentiation and whether transient receptor potential vanilloid 1 (TRPV1) played a key role in dry mouth. The induction of dry mouth due to salivary gland resection (SGR) was confirmed on the basis of reduced salivation and disrupted fungiform papillae. The expression of keratin 8 (K8) of taste bud cells, neurofilament (NF), sonic hedgehog (Shh), and glioma-associated oncogene homolog 1 (Gli1) around taste bud cells was downregulated; however, the expression of TRPV1, P2X purinoceptor 3 (P2X3), and hematopoietic stem cell factor (c-Kit) was upregulated at the NF ends in the dry mouth group. To investigate the effect of TRPV1 defect on dry mouth, we induced dry mouth in the TRPV-/- group. The K8, NF, and P2X3 expression patterns were the same in the TRPV1 wild-type and TRPV1-/- dry mouth groups. However, Shh and c-Kit expression decreased regardless of dry mouth in the case of TRPV1 deficiency. These results indicated that TRPV1 positively regulated proliferation during taste bud cell injury by blocking the Shh/Gli1 pathway. In addition, not only cell proliferation but also differentiation of taste bud cells could not be regulated under TRPV1-deficiency conditions. Thus, TRPV1 positively regulates taste bud cell innervation and differentiation; this finding could be valuable in the clinical treatment of dry mouth-related taste dysfunction.

Introduction: Scar treatments aim to address pathologic collagen deposition; however, they can be expensive or difficult to control. Electrochemical therapy (ECT) offers a simple alternative treatment. The purpose of this study is to examine the acid-base and histological changes in ex vivo human abdominal skin following ECT. Methods: Forty-two ex vivo human panniculus tissue sections collected from six individuals were tumesced with normal saline. ECT was performed by inserting two platinum needle electrodes connected to a DC power supply into each specimen. Voltage was varied (3–6 V) and applied for 5 minutes. Each specimen was sectioned across both electrode insertion sites and immediately stained with pH sensitive dye. The width of dye color change for each dosimetry pair was calculated. Hematoxylin and eosin staining was used to evaluate samples. Results and Discussion: ECT caused a spatially localised and dose-dependent increased area of acidic and basic pH around the anode and cathode, respectively. A significantly greater mean width of pH change was generated at the cathode compared to the anode in all treatment groups. Histological evaluation displayed broad condensation and hyalinisation of dermal collagen. Conclusion: ECT triggered dermal pH alterations and changed the underlying structural framework of the specimen. This technology may serve as a low-cost, minimally invasive local soft-tissue remodeling technique with potential application in scar management. Level of Evidence: 5 Lay Summary Electrochemical therapy is a novel treatment that causes spatially selective dermal injury in areas of interest. This study measures the effects of electrochemical therapy when applied to abdominal skin. Electrochemical therapy appears to have beneficial effects by causing a highly localised reduction in collagen content or local softening of tissue, which is consistent with other studies on scar therapies, including chemexfoliation, radiofrequency technologies, and lasers. However, electrochemical therapy can be performed at a fraction of the costs of these aforementioned modalities.

BackgroundMeibomian glands exist beneath the palpebral conjunctiva; thus, it is invisible to the naked eye without infrared imaging. This study used meibography to group patients with meibomian gland dysfunction (MGD) and assessed the effects of hyperthermic massage and mechanical squeezing in both groups.Materials and methodsPatients with MGD were divided into two groups, according to the degree of meibomian gland loss: group 1, in which the sum of eyelid scores ranged from 0 to 4 (mild to moderate gland loss) and group 2, in which the sum of eyelid scores ranged from 5 to 6 (severe gland loss). Hyperthermic massage and mechanical squeezing were given to both groups once a week for 4 weeks, and only non-preservative artificial tears were allowed. Ocular surface disease index (OSDI), Schirmer's test, meibography score, tear break-up time (TBUT), ocular surface staining, expressible meibomian gland, and quality before and after treatment were compared.ResultsOf the 49 patients who completed the 4 weeks of treatment and the evaluation at week 5, 29 were assigned to group 1 and 20 were assigned to group 2. Meibography scores, OSDI, TBUT, and expressibility of meibum had significant differences before and after treatments in both groups. However, there was no significant difference between the changes in clinical signs between group 1 and 2 after treatment. Without grouping, all patients showed significant decreases in meibography score, OSDI, cornea staining score, and increases in TBUT and expressibility of meibum after treatment.ConclusionsConsidering the results of the current study, hyperthermic massage and mechanical squeezing may be effective in patients with meibomian gland loss, regardless of the degree of severity.

Alzheimer’s disease (AD) is the most frequent cause of age-related neurodegeneration and cognitive impairment, and there are currently no broadly effective therapies. The underlying pathogenesis is complex, but a growing body of evidence implicates mitochondrial dysfunction as a common pathomechanism involved in many of the hallmark features of the AD brain, such as formation of amyloid-beta (Aβ) aggregates (amyloid plaques), neurofibrillary tangles, cholinergic system dysfunction, impaired synaptic transmission and plasticity, oxidative stress, and neuroinflammation, that lead to neurodegeneration and cognitive dysfunction. Indeed, mitochondrial dysfunction concomitant with progressive accumulation of mitochondrial Aβ is an early event in AD pathogenesis. Healthy mitochondria are critical for providing sufficient energy to maintain endogenous neuroprotective and reparative mechanisms, while disturbances in mitochondrial function, motility, fission, and fusion lead to neuronal malfunction and degeneration associated with excess free radical production and reduced intracellular calcium buffering. In addition, mitochondrial dysfunction can contribute to amyloid-β precursor protein (APP) expression and misprocessing to produce pathogenic fragments (e.g., Aβ1-40). Given this background, we present an overview of the importance of mitochondria for maintenance of neuronal function and how mitochondrial dysfunction acts as a driver of cognitive impairment in AD. Additionally, we provide a brief summary of possible treatments targeting mitochondrial dysfunction as therapeutic approaches for AD.

We recently reported that adeno-associated virus serotype 1-constitutively active Ras homolog enriched in brain [AAV1-Rheb(S16H)] transduction of hippocampal neurons could induce neuron-astroglia interactions in the rat hippocampus in vivo, resulting in neuroprotection. However, it remains uncertain whether AAV1-Rheb(S16H) transduction induces neurotrophic effects and preserves the cognitive memory in an animal model of Alzheimer’s disease (AD) with characteristic phenotypic features, such as β-amyloid (Aβ) accumulation and cognitive impairments. To assess the therapeutic potential of Rheb(S16H) in AD, we have examined the beneficial effects of AAV1-Rheb(S16H) administration in the 5XFAD mouse model. Rheb(S16H) transduction of hippocampal neurons in the 5XFAD mice increased the levels of neurotrophic signaling molecules, including brain-derived neurotrophic factor (BDNF) and ciliary neurotrophic factor (CNTF), and their corresponding receptors, tropomyosin receptor kinase B (TrkB) and CNTF receptor α subunit (CNTFRα), respectively. In addition, Rheb(S16H) transduction inhibited Aβ production and accumulation in the hippocampus of 5XFAD mice and protected the decline of long-term potentiation (LTP), resulting in the prevention of cognitive impairments, which was demonstrated using novel object recognition testing. These results indicate that Rheb(S16H) transduction of hippocampal neurons may have therapeutic potential in AD by inhibiting Aβ accumulation and preserving LTP associated with cognitive memory.

This article presents a recursive solution to the power-transmission loss in DC-powered networks. In such a network, the load cannot be modeled as a fixed equivalent resistance value, since the switching regulator may draw more or less current based on the actual supply voltage to meet the power demand. Although the power-transmission loss itself is simply I2 RL, I, in turn, depends on the load’s supply voltage, which, in turn, depends on I, making it impossible to derive a closed-form solution by classical resistive network analysis in general. The proposed approach is to first derive a closed-form solution to I in the one-node topology using the quadratic formula. Next, we extend our solution to a locally daisy-chained (LDC) network, where the network is readily decomposable into stages, such that the solution combines the closed-form formula for the current stage with the recursive solution for the subsequent stages. We then generalize the LDC topology to trees. In practice, the solution converges quickly after a small number of iterations. It has been validated on real-life networks, such as power over controller area network (PoCAN).

Prognostic models are often used to estimate the length of patient survival. The Cox proportional hazards model has traditionally been applied to assess the accuracy of prognostic models. However, it may be suboptimal due to the inflexibility to model the baseline survival function and when the proportional hazards assumption is violated. The aim of this study was to use internal validation to compare the predictive power of a flexible Royston-Parmar family of survival functions with the Cox proportional hazards model. We applied the Palliative Performance Scale on a dataset of 590 hospice patients at the time of hospice admission. The retrospective data were obtained from the Lifepath Hospice and Palliative Care center in Hillsborough County, Florida, USA. The criteria used to evaluate and compare the models' predictive performance were the explained variation statistic R(2), scaled Brier score, and the discrimination slope. The explained variation statistic demonstrated that overall the Royston-Parmar family of survival functions provided a better fit (R(2) =0.298; 95% CI: 0.236-0.358) than the Cox model (R(2) =0.156; 95% CI: 0.111-0.203). The scaled Brier scores and discrimination slopes were consistently higher under the Royston-Parmar model. Researchers involved in prognosticating patient survival are encouraged to consider the Royston-Parmar model as an alternative to Cox.

Purpose: The development of optics-based wearables for bladder volume monitoring has emerged as a significant topic in recent years. Given the innovative nature of this technology, there is currently no bladder phantom available to effectively validate these devices against more established gold standards, such as ultrasound. In this study, we showcase and demonstrate the performance of our hybrid bladder phantom by using an optical device and making comparisons with ultrasound. Methods: A series of validation tests, including phantom repeatability, ultrasound scanning, and an optical test, were performed. A near-infrared optical device was utilized to conduct diffuse optical spectroscopy (DOS). Machine learning models were employed to construct predictive models of volume using optical signals. Results: The size and position of an embedded balloon, serving as an analog for the bladder, were shown to be consistent when infused with 100 mL to 350 mL of water during repeatability testing. For DOS data, we present 7 types of machine learningbased models based on different optical signals. The 2 best-performing models demonstrated an average absolute volume error ranging from 12.7 mL to 19.0 mL. Conclusions: In this study, we introduced a hybrid bladder phantom designed for the validation of near-infrared spectroscopy-based bladder monitoring devices in comparison with ultrasound techniques. By offering a reproducible and robust validation tool, we aim to support the advancement of next-generation optical wearables for bladder volume monitoring. [ABSTRACT FROM AUTHOR]