Current Novel Targeted Therapeutic Strategies in Multiple Myeloma.

Multiple myeloma (MM) is a hematologic malignancy caused by the clonal expansion of immunoglobulin-producing plasma cells in the bone marrow and/or extramedullary sites. Common manifestations of MM include anemia, renal dysfunction, infection, bone pain, hypercalcemia, and fatigue. Despite numerous recent advancements in the MM treatment paradigm, current therapies demonstrate limited long-term effectiveness and eventual disease relapse remains exceedingly common. Myeloma cells often develop drug resistance through clonal evolution and alterations of cellular signaling pathways. Therefore, continued research of new targets in MM is crucial to circumvent cumulative drug resistance, overcome treatment-limiting toxicities, and improve outcomes in this incurable disease. This article provides a comprehensive overview of the landscape of novel treatments and emerging therapies for MM grouped by molecular target. Molecular targets outlined include BCMA, GPRC5D, FcRH5, CD38, SLAMF7, BCL-2, kinesin spindle protein, protein disulfide isomerase 1, peptidylprolyl isomerase A, Sec61 translocon, and cyclin-dependent kinase 6. Immunomodulatory drugs, NK cell therapy, and proteolysis-targeting chimera are described as well.

Securing the Insecure: A First-Line-of-Defense for Body-Centric Nanoscale Communication Systems Operating in THz Band.

This manuscript presents a novel mechanism (at the physical layer) for authentication and transmitter identification in a body-centric nanoscale communication system operating in the terahertz (THz) band. The unique characteristics of the propagation medium in the THz band renders the existing techniques (say for impersonation detection in cellular networks) not applicable. In this work, we considered a body-centric network with multiple on-body nano-senor nodes (of which some nano-sensors have been compromised) who communicate their sensed data to a nearby gateway node. We proposed to protect the transmissions on the link between the legitimate nano-sensor nodes and the gateway by exploiting the path loss of the THz propagation medium as the fingerprint/feature of the sender node to carry out authentication at the gateway. Specifically, we proposed a two-step hypothesis testing mechanism at the gateway to counter the impersonation (false data injection) attacks by malicious nano-sensors. To this end, we computed the path loss of the THz link under consideration using the high-resolution transmission molecular absorption (HITRAN) database. Furthermore, to refine the outcome of the two-step hypothesis testing device, we modeled the impersonation attack detection problem as a hidden Markov model (HMM), which was then solved by the classical Viterbi algorithm. As a bye-product of the authentication problem, we performed transmitter identification (when the two-step hypothesis testing device decides no impersonation) using (i) the maximum likelihood (ML) method and (ii) the Gaussian mixture model (GMM), whose parameters are learned via the expectation-maximization algorithm. Our simulation results showed that the two error probabilities (missed detection and false alarm) were decreasing functions of the signal-to-noise ratio (SNR). Specifically, at an SNR of 10 dB with a pre-specified false alarm rate of 0.2, the probability of correct detection was almost one. We further noticed that the HMM method outperformed the two-step hypothesis testing method at low SNRs (e.g., a 10% increase in accuracy was recorded at SNR = -5 dB), as expected. Finally, it was observed that the GMM method was useful when the ground truths (the true path loss values for all the legitimate THz links) were noisy.

Hardware-Based Hopfield Neuromorphic Computing for Fall Detection.

With the popularity of smart wearable systems, sensor signal processing poses more challenges to machine learning in embedded scenarios. For example, traditional machine-learning methods for data classification, especially in real time, are computationally intensive. The deployment of Artificial Intelligence algorithms on embedded hardware for fast data classification and accurate fall detection poses a huge challenge in achieving power-efficient embedded systems. Therefore, by exploiting the associative memory feature of Hopfield Neural Network, a hardware module has been designed to simulate the Neural Network algorithm which uses sensor data integration and data classification for recognizing the fall. By adopting the Hebbian learning method for training neural networks, weights of human activity features are obtained and implemented/embedded into the hardware design. Here, the neural network weight of fall activity is achieved through data preprocessing, and then the weight is mapped to the amplification factor setting in the hardware. The designs are checked with validation scenarios, and the experiment is completed with a Hopfield neural network in the analog module. Through simulations, the classification accuracy of the fall data reached 88.9% which compares well with some other results achieved by the software-based machine-learning algorithms, which verify the feasibility of our hardware design. The designed system performs the complex signal calculations of the hardware's feedback signal, replacing the software-based method. A straightforward circuit design is used to meet the weight setting from the Hopfield neural network, which is maximizing the reusability and flexibility of the circuit design.

Salvage Radical Prostatectomy after Primary Focal Ablative Therapy: A Systematic Review and Meta-Analysis.

CONTEXT: Focal therapy (FT) has been gaining popularity as a treatment option for localized intermediate-risk prostate cancer (PCa) due to the associated lower morbidity compared to whole-gland treatment. However, there is an increased risk of local cancer recurrence requiring subsequent treatment in a small proportion of patients. OBJECTIVE: To conduct a systematic review and meta-analysis to better describe and analyze patient postoperative, oncologic, and functional outcomes for those who underwent salvage radical prostatectomy (sRP) to manage their primary FT failure. EVIDENCE ACQUISITION: A systematic review was completed using three databases (PubMed, Embase, and CINAHL) from October to December 2021 to identify data on outcomes in patients who received sRP for cancer recurrence after prior focal treatment. EVIDENCE SYNTHESIS: 12 articles (482 patients) were included. Median time to sRP was 24 months. Median follow-up time was 27 months. A meta-analysis revealed a postoperative complication rate of 15% (95% CI: 0.09, 0.24), with 4.6% meeting criteria for a major complication Clavien (CG) grade ≥3. Severe GU toxicity was seen in 3.6% of the patients, and no patients had severe GI toxicity. Positive surgical margins (PSM) were found in 27% (95% CI: 0.19, 0.37). Biochemical recurrence (BCR) after sRP occurred in 23% (95% CI: 0.17, 0.30), indicating a BCR-free probability of 77% at 2 years. Continence (pad-free) and potency (ability to have penetrative sex) were maintained in 67% (95% CI: 0.53, 0.78) and 37% (95% CI: 0.18, 0.62) at 12 months, respectively. CONCLUSION: Our evidence shows acceptable complication rates and oncologic outcomes; however, with suboptimal functional outcomes for patients undergoing sRP for recurrent PCa after prior FT. Inferior outcomes were observed for salvage treatment compared to primary radical prostatectomy (pRP). More high-quality studies are needed to better characterize outcomes after this sequence of PCa treatments. PATIENT SUMMARY: We looked at treatment outcomes and toxicity for men treated with sRP for prior FT failure. We conclude that these patients will have significant detriment to genitourinary function, with outcomes being worse than those for pRP patients.

Dental caries detection using a semi-supervised learning approach.

Early diagnosis of dental caries progression can prevent invasive treatment and enable preventive treatment. In this regard, dental radiography is a widely used tool to capture dental visuals that are used for the detection and diagnosis of caries. Different deep learning (DL) techniques have been used to automatically analyse dental images for caries detection. However, most of these techniques require large-scale annotated data to train DL models. On the other hand, in clinical settings, such medical images are scarcely available and annotations are costly and time-consuming. To this end, we present an efficient self-training-based method for caries detection and segmentation that leverages a small set of labelled images for training the teacher model and a large collection of unlabelled images for training the student model. We also propose to use centroid cropped images of the caries region and different augmentation techniques for the training of self-supervised models that provide computational and performance gains as compared to fully supervised learning and standard self-supervised learning methods. We present a fully labelled dental radiographic dataset of 141 images that are used for the evaluation of baseline and proposed models. Our proposed self-supervised learning strategy has provided performance improvement of approximately 6% and 3% in terms of average pixel accuracy and mean intersection over union, respectively as compared to standard self-supervised learning. Data and code will be made available to facilitate future research.

Muscle progenitor cells are required for skeletal muscle regeneration and prevention of adipogenesis after limb ischemia.

Skeletal muscle injury in peripheral artery disease (PAD) has been attributed to vascular insufficiency, however evidence has demonstrated that muscle cell responses play a role in determining outcomes in limb ischemia. Here, we demonstrate that genetic ablation of Pax7+ muscle progenitor cells (MPCs) in a model of hindlimb ischemia (HLI) inhibited muscle regeneration following ischemic injury, despite a lack of morphological or physiological changes in resting muscle. Compared to control mice (Pax7WT), the ischemic limb of Pax7-deficient mice (Pax7Δ) was unable to generate significant force 7 or 28 days after HLI. A significant increase in adipose was observed in the ischemic limb 28 days after HLI in Pax7Δ mice, which replaced functional muscle. Adipogenesis in Pax7Δ mice corresponded with a significant increase in PDGFRα+ fibro/adipogenic progenitors (FAPs). Inhibition of FAPs with batimastat decreased muscle adipose but increased fibrosis. In vitro, Pax7Δ MPCs failed to form myotubes but displayed increased adipogenesis. Skeletal muscle from patients with critical limb threatening ischemia displayed increased adipose in more ischemic regions of muscle, which corresponded with fewer satellite cells. Collectively, these data demonstrate that Pax7+ MPCs are required for muscle regeneration after ischemia and suggest that muscle regeneration may be an important therapeutic target in PAD.

Schema and cancer detection rates for transperineal prostate biopsy templates: a review.

Prostate cancer (PCa) is the most common noncutaneous malignancy in men and is the second leading cause of cancer mortality in men in the United States. Current practice requires histopathological confirmation of cancer achieved through biopsy for diagnosis. The transrectal approach for prostate biopsy has been the standard for several decades. However, the risks and limitations of transrectal biopsies have led to a recent resurgence of transperineal prostatic biopsies. Recent studies have demonstrated the transperineal approach for prostate biopsies to be effective, associated with minimal complications and superior in several aspects to traditional transrectal biopsies. While sextant and extended sextant templates are widely accepted templates for transrectal biopsy, there are a diverse set of transperineal biopsy templates available for use, without consensus on the optimal sampling strategy. We aim to critically appraise the salient features of established transperineal biopsy templates.

Privacy-Preserving British Sign Language Recognition Using Deep Learning.

Sign language is a means of communication between the deaf community and normal hearing people who use hand gestures, facial expressions, and body language to communicate. It has the same level of complexity as spoken language, but it does not employ the same sentence structure as English. The motions in sign language comprise a range of distinct hand and finger articulations that are occasionally synchronized with the head, face, and body. Existing sign language recognition systems are mainly camera-based, which have fundamental limitations of poor lighting conditions, potential training challenges with longer video sequence data, and serious privacy concerns. This study presents a first of its kind, contact-less and privacy-preserving British sign language (BSL) Recognition system using Radar and deep learning algorithms. Six most common emotions are considered in this proof of concept study, namely confused, depressed, happy, hate, lonely, and sad. The collected data is represented in the form of spectrograms. Three state-of-the-art deep learning models, namely, InceptionV3, VGG19, and VGG16 models then extract spatiotemporal features from the spectrogram. Finally, BSL emotions are accurately identified by classifying the spectrograms into considered emotion signs. Comparative simulation results demonstrate that a maximum classifying accuracy of 93.33% is obtained on all classes using the VGG16 model.

Pushing the limits of remote RF sensing by reading lips under the face mask.

The problem of Lip-reading has become an important research challenge in recent years. The goal is to recognise speech from lip movements. Most of the Lip-reading technologies developed so far are camera-based, which require video recording of the target. However, these technologies have well-known limitations of occlusion and ambient lighting with serious privacy concerns. Furthermore, vision-based technologies are not useful for multi-modal hearing aids in the coronavirus (COVID-19) environment, where face masks have become a norm. This paper aims to solve the fundamental limitations of camera-based systems by proposing a radio frequency (RF) based Lip-reading framework, having an ability to read lips under face masks. The framework employs Wi-Fi and radar technologies as enablers of RF sensing based Lip-reading. A dataset comprising of vowels A, E, I, O, U and empty (static/closed lips) is collected using both technologies, with a face mask. The collected data is used to train machine learning (ML) and deep learning (DL) models. A high classification accuracy of 95% is achieved on the Wi-Fi data utilising neural network (NN) models. Moreover, similar accuracy is achieved by VGG16 deep learning model on the collected radar-based dataset.

Making assembly line in supply chain robust and secure using UHF RFID.

This paper presents a block-chain enabled inkjet-printed ultrahigh frequency radiofrequency identification (UHF RFID) system for the supply chain management, traceability and authentication of hard to tag bottled consumer products containing fluids such as water, oil, juice, and wine. In this context, we propose a novel low-cost, compact inkjet-printed UHF RFID tag antenna design for liquid bottles, with 2.5 m read range improvement over existing designs along with robust performance on different liquid bottle products. The tag antenna is based on a nested slot-based configuration that achieves good impedance matching around high permittivity surfaces. The tag was designed and optimized using the characteristic mode analysis. Moreover, the proposed RFID tag was commercially tested for tagging and billing of liquid bottle products in a conveyer belt and smart refrigerator for automatic billing applications. With the help of block-chain based product tracking and a mobile application, we demonstrate a real-time, secure and smart supply chain process in which items can be monitored using the proposed RFID technology. We believe the standalone system presented in this paper can be deployed to create smart contracts that benefit both the suppliers and consumers through the development of trust. Furthermore, the proposed system will paves the way towards authentic and contact-less delivery of food, drinks and medicine in recent Corona virus pandemic.

Predicting long-term type 2 diabetes with support vector machine using oral glucose tolerance test.

Diabetes is a large healthcare burden worldwide. There is substantial evidence that lifestyle modifications and drug intervention can prevent diabetes, therefore, an early identification of high risk individuals is important to design targeted prevention strategies. In this paper, we present an automatic tool that uses machine learning techniques to predict the development of type 2 diabetes mellitus (T2DM). Data generated from an oral glucose tolerance test (OGTT) was used to develop a predictive model based on the support vector machine (SVM). We trained and validated the models using the OGTT and demographic data of 1,492 healthy individuals collected during the San Antonio Heart Study. This study collected plasma glucose and insulin concentrations before glucose intake and at three time-points thereafter (30, 60 and 120 min). Furthermore, personal information such as age, ethnicity and body-mass index was also a part of the data-set. Using 11 OGTT measurements, we have deduced 61 features, which are then assigned a rank and the top ten features are shortlisted using minimum redundancy maximum relevance feature selection algorithm. All possible combinations of the 10 best ranked features were used to generate SVM based prediction models. This research shows that an individual's plasma glucose levels, and the information derived therefrom have the strongest predictive performance for the future development of T2DM. Significantly, insulin and demographic features do not provide additional performance improvement for diabetes prediction. The results of this work identify the parsimonious clinical data needed to be collected for an efficient prediction of T2DM. Our approach shows an average accuracy of 96.80% and a sensitivity of 80.09% obtained on a holdout set.

Machine learning driven non-invasive approach of water content estimation in living plant leaves using terahertz waves.

BACKGROUND: The demand for effective use of water resources has increased because of ongoing global climate transformations in the agriculture science sector. Cost-effective and timely distributions of the appropriate amount of water are vital not only to maintain a healthy status of plants leaves but to drive the productivity of the crops and achieve economic benefits. In this regard, employing a terahertz (THz) technology can be more reliable and progressive technique due to its distinctive features. This paper presents a novel, and non-invasive machine learning (ML) driven approach using terahertz waves with a swissto12 material characterization kit (MCK) in the frequency range of 0.75 to 1.1 THz in real-life digital agriculture interventions, aiming to develop a feasible and viable technique for the precise estimation of water content (WC) in plants leaves for 4 days. For this purpose, using measurements observations data, multi-domain features are extracted from frequency, time, time-frequency domains to incorporate three different machine learning algorithms such as support vector machine (SVM), K-nearest neighbour (KNN) and decision-tree (D-Tree). RESULTS: The results demonstrated SVM outperformed other classifiers using tenfold and leave-one-observations-out cross-validation for different days classification with an overall accuracy of 98.8%, 97.15%, and 96.82% for Coffee, pea shoot, and baby spinach leaves respectively. In addition, using SFS technique, coffee leaf showed a significant improvement of 15%, 11.9%, 6.5% in computational time for SVM, KNN and D-tree. For pea-shoot, 21.28%, 10.01%, and 8.53% of improvement was noticed in operating time for SVM, KNN and D-Tree classifiers, respectively. Lastly, baby spinach leaf exhibited a further improvement of 21.28% in SVM, 10.01% in KNN, and 8.53% in D-tree in overall operating time for classifiers. These improvements in classifiers produced significant advancements in classification accuracy, indicating a more precise quantification of WC in leaves. CONCLUSION: Thus, the proposed method incorporating ML using terahertz waves can be beneficial for precise estimation of WC in leaves and can provide prolific recommendations and insights for growers to take proactive actions in relations to plants health monitoring.

Laparoscopic resection of type I choledochal cyst in an adult and Roux-en-Y hepaticojejunostomy: a case report and literature review.

Choledochal cysts are rare cystic dilatations of the extrahepatic biliary tree, the intrahepatic bile ducts, or both and carry a substantial risk of malignant transformation. Type I choledochal cysts, which involve the entire common hepatic and common bile ducts, represent 80% to 90% of these lesions. We report laparoscopic excision of symptomatic type I choledochal cyst in a 37-year-old woman, and review the literature. Laparoscopic excision of the extrahepatic biliary tree from the hepatic confluence to the anomalous pancreatobiliary junction with en bloc cholecystectomy and reconstruction with a Roux-en-Y hepaticojejunostomy was accomplished. Postoperative recovery was uneventful with a hospital stay of 3 days. She remains well and asymptomatic at 6 months of follow-up. Laparoscopic excision of choledochal cysts may be safely accomplished with a prompt recovery. Further experience with this approach in larger number of patients is justified and long-term follow-up data are needed.

Clinical and Biochemical Parameters of Hemodialysis Patients Before and During Islamic Month of Ramadan.

INTRODUCTION: Month of Ramadan bring many changes in life style, especially the diet of Muslims all over the world among both fasting and nonfasting individuals. Hemodialysis patients are kept on restricted diet because of fluid and electrolytes imbalance. The aim of this study was to compare changes in the clinical and biochemical parameters in fasting and nonfasting hemodialysis patients during the Ramadan. MATERIALS AND METHODS: In a longitudinal study, we recruited 282 patients who were on maintenance dialysis for more than 3 months. Measurements included body weight, blood pressure, serum potassium, serum albumin, and serum phosphorus at the beginning and during the last week of Ramadan. RESULTS: There were 252 patients who were not fasted while 34 patients were those who fasted during the Ramadan. In the nonfasting hemodialysis patients, serum albumin significantly increased at the end of Ramadan (P < .001), while serum phosphorus levels (P = .004) and diastolic blood pressure (P = .002) showed a decrease as compared with the measurements before Ramadan. In the fasting group, only serum albumin had a significant increase (P < .001) during Ramadan, while other parameters were not significantly different between the two measurements. CONCLUSIONS: Changes in dietary pattern and content during the Ramadan is safe in terms of electrolyte balance and blood pressure changes for patients on hemodialysis. It is also safe for those patients who want to fast during this month.

Mortality trend among hemodialysis patients during the islamic month of Ramadan: a 24 years retrospective study.

OBJECTIVE: The effect of month of Ramadan on the mortality in hemodialysis patients, and to compare it with that in all other Islamic months. STUDY DESIGN: A descriptive study. PLACE AND DURATION OF STUDY: Hemodialysis Unit, The Kidney Center, Karachi, from January 1989 to December 2012. METHODOLOGY: All those patients who were diagnosed to have end stage kidney disease and on maintenance hemodialysis for more than 3 months, regardless of underlying cause of kidney failure were included. Patients with acute kidney injury were excluded. Status of the patients was recorded at the end of the study period. The fasting status of the patients was not mentioned. The deaths of the patients were further evaluated and frequencies of death in all twelve Islamic months were calculated. RESULTS: A total of 1,841 patients were registered, out of whom 897 (48.7%) died, and 269 (14.6%) survived till the end of the study. One thousand and fifty six (57.3%) were males, 651 (35.4%) were diabetic. Total number of 143 (7.76%) events occurred in Ramadan, out of which 94 patients died which make nearly 11% of the total deaths distributed in 12 Islamic months. Frequency of death was higher in Ramadan when compared with other months. CONCLUSION: Ramadan reflected a higher frequency of death. Therefore, there is a need to evaluate the risk factors in a prospective study so that the dialysis patients can be better managed during this period.

Biochemical nutritional parameters and their impact on hemodialysis efficiency.

To determine the nutritional status of chronic hemodialysis (HD) patients and the association of changes in serum albumin levels, C-reactive protein (CRP), low density lipoprotein (LDL) cholesterol and body mass index (BMI) as indicators of nutritional status with the urea reduction ratio (URR) during dialysis, we studied 201 chronic HD patients (97 males and the mean age was 51 +/- 15 years). Diabetes was the cause of chronic kidney disease (CKD) in 34% of the patients, hypertension in 57%, chronic glomerulonephritis in 12%, and obstructive uropathy in 10%. BMI less than 18.5 (under weight) was found in 17% of patients, more 18.5 but less than 25 (normal) in 56%, more than 25 but less than 30 (overweight) in 21%, and more than 30 (obese) in 6%. The laboratory investigations revealed hypercalcemia in 62% of the patients (15 patients were found to have tertiary hyperparathyroidism), total cholesterol less than 100 mg/dL in 6% (mean 152 +/- 37.5 mg/dL), and URR of less than 60% in 12% of patients and greater than 60 but less than 65% in 33%. Hypoalbuminemia was associated with poor URR (P < 0.05), whereas no statistically significant correlation was found between URR and iPTH, LDL cholesterol, CRP and body mass index. We conclude that poor nutritional status was detected among a significant number of our patients with poor dietary education. Increased risk of malnutrition was significantly associated with older age and inadequate dialysis dose. Hypoalbuminemia was the single most important factor associated with poor URR.