Discovery of LNP1892: A Precision Calcimimetic for the Treatment of Secondary Hyperparathyroidism.

The calcium sensing receptor (CaSR) plays an important role in maintaining calcium homeostasis. The use of calcimimetic cinacalcet has been established to activate CaSR and normalize hypercalcemia. However, cinacalcet has limitations due to its high cLogP and pKa. A systematic optimization of cinacalcet to reduce its cLogP and pKa yielded compound 23a (LNP1892). Compound 23a showed excellent potency and a favorable pharmacokinetics profile, and lacked the liabilities of cinacalcet, making it a highly differentiated precision calcimimetic. In adenine-diet-induced chronic kidney disease (CKD) models, 23a demonstrated robust and dose-dependent efficacy, as measured by plasma parathyroid hormone (PTH) levels. It also showed an excellent safety profile in animal studies. Phase 1 clinical trials with 23a in healthy volunteers confirmed its excellent safety, tolerability, and effectiveness in lowering PTH levels in a dose-dependent manner, without causing symptomatic hypocalcaemia. Encouraged by these promising results, LNP1892 was taken to a Phase 2 study in CKD patients.

MCSC-Net: COVID-19 detection using deep-Q-neural network classification with RFNN-based hybrid whale optimization.

BACKGROUND: COVID-19 is the most dangerous virus, and its accurate diagnosis saves lives and slows its spread. However, COVID-19 diagnosis takes time and requires trained professionals. Therefore, developing a deep learning (DL) model on low-radiated imaging modalities like chest X-rays (CXRs) is needed. OBJECTIVE: The existing DL models failed to diagnose COVID-19 and other lung diseases accurately. This study implements a multi-class CXR segmentation and classification network (MCSC-Net) to detect COVID-19 using CXR images. METHODS: Initially, a hybrid median bilateral filter (HMBF) is applied to CXR images to reduce image noise and enhance the COVID-19 infected regions. Then, a skip connection-based residual network-50 (SC-ResNet50) is used to segment (localize) COVID-19 regions. The features from CXRs are further extracted using a robust feature neural network (RFNN). Since the initial features contain joint COVID-19, normal, pneumonia bacterial, and viral properties, the conventional methods fail to separate the class of each disease-based feature. To extract the distinct features of each class, RFNN includes a disease-specific feature separate attention mechanism (DSFSAM). Furthermore, the hunting nature of the Hybrid whale optimization algorithm (HWOA) is used to select the best features in each class. Finally, the deep-Q-neural network (DQNN) classifies CXRs into multiple disease classes. RESULTS: The proposed MCSC-Net shows the enhanced accuracy of 99.09% for 2-class, 99.16% for 3-class, and 99.25% for 4-class classification of CXR images compared to other state-of-art approaches. CONCLUSION: The proposed MCSC-Net enables to conduct multi-class segmentation and classification tasks applying to CXR images with high accuracy. Thus, together with gold-standard clinical and laboratory tests, this new method is promising to be used in future clinical practice to evaluate patients.

A comparative study of perioperative techniques to attain negative margins and spare healthy breast tissue in breast conserving surgery.

BACKGROUND AND AIM: Traditionally lumpectomy as a part of breast-conserving surgery (BCS) is performed by palpation-guided method leading to positive margins and large excision volumes. There is no evidence suggesting that wide margin excisions decrease intra-breast tumour recurrence. Various perioperative techniques are used for margin assessment. We aimed to compare three commonly used techniques, i.e., ultrasound-guided surgery, palpation-guided surgery and cavity shaving for attaining negative margins and estimating the extent of healthy breast tissue resection. METHOD: A prospective comparative study was performed on 90 patients who underwent breast conservation surgery for early breast cancer between August 2018 and June 2019. Tumour excision with a minimum of 1 cm margin was done either using ultrasound, palpation or cavity shaving. Histopathological evaluation was done to assess the margin status and excess amount of resected normal breast tissue. Calculated resection ratio (CRR) defining the excess amount of the resected breast tissue was achieved by dividing the total resection volume (TRV) by optimal resection volume (ORV). The time taken for excision was also recorded. RESULTS: Histopathology of all 90 patients (30 in each group) revealed a negative resection margin in 93.3% of 30 patients in palpation-guided surgery group and 100% in both ultrasound-guided surgery and cavity shaving groups. Two patients (6.7%) from the cavity shaving group had positive margins on initial lumpectomy but shave margins were negative. TRV was significantly less in the ultrasound-guided surgery group compared to the palpation-guided surgery group and cavity shaving group (76.9 cm3, 94.7 cm3 and 126.3 cm3 respectively; p < 0.0051). CRR was 1.2 in ultrasound group compared to 1.9 in palpation group and 2.1 in cavity shave group which was also statistically significant (p < 0.0001).Excision time was significantly less (p < 0.001) in palpation-guided surgery group (13.8 min) compared to cavity shaving group (15.1 min) and ultrasound-guided group (19.4 min). CONCLUSION: Ultrasound-guided surgery is more accurate in attaining negative margins with the removal of least amount of healthy breast tissue compared to palpation-guided surgery and cavity shaving.

Machine Learning Methods for Attack Detection in the Smart Grid.

Attack detection problems in the smart grid are posed as statistical learning problems for different attack scenarios in which the measurements are observed in batch or online settings. In this approach, machine learning algorithms are used to classify measurements as being either secure or attacked. An attack detection framework is provided to exploit any available prior knowledge about the system and surmount constraints arising from the sparse structure of the problem in the proposed approach. Well-known batch and online learning algorithms (supervised and semisupervised) are employed with decision- and feature-level fusion to model the attack detection problem. The relationships between statistical and geometric properties of attack vectors employed in the attack scenarios and learning algorithms are analyzed to detect unobservable attacks using statistical learning methods. The proposed algorithms are examined on various IEEE test systems. Experimental analyses show that machine learning algorithms can detect attacks with performances higher than attack detection algorithms that employ state vector estimation methods in the proposed attack detection framework.

Obturator hernia: A diagnostic challenge.

INTRODUCTION: Obturator hernia is an extremely rare type of hernia with relatively high mortality and morbidity. Its early diagnosis is challenging since the signs and symptoms are non specific. PRESENTATION OF CASE: Here in we present a case of 70 years old women who presented with complaints of intermittent colicky abdominal pain and vomiting. Plain radiograph of abdomen showed acute dilatation of stomach. Ultrasonography showed small bowel obstruction at the mid ileal level with evidence of coiled loops of ileum in pelvis. On exploration, Right Obstructed Obturator hernia was found. The obstructed Intestine was reduced and resected and the obturator foramen was closed with simple sutures. Postoperative period was uneventful. DISCUSSION: Obturator hernia is a rare pelvic hernia and poses a diagnostic challenge. Obturator hernia occurs when there is protrusion of intra-abdominal contents through the obturator foramen in the pelvis. The signs and symptoms are non specific and generally the diagnosis is made during exploration for the intestinal obstruction, one of the four cardinal features. Others are pain on the medial aspect of thigh called as Howship Rombergs sign, repeated attacks of Intestinal Obstruction and palpable mass on the medial aspect of thigh. CONCLUSION: Obturator hernia is a rare but significant cause of intestinal obstruction especially in emaciated elderly woman and a diagnostic challenge for the Doctors. CT scan is valuable to establish preoperative diagnosis. Surgery either open or laproscopic, is the only treatment. The need for the awareness is stressed and CT scan can be helpful.

Regularized variational Bayesian learning of echo state networks with delay&sum readout.

In this work, a variational Bayesian framework for efficient training of echo state networks (ESNs) with automatic regularization and delay&sum (D&S) readout adaptation is proposed. The algorithm uses a classical batch learning of ESNs. By treating the network echo states as fixed basis functions parameterized with delay parameters, we propose a variational Bayesian ESN training scheme. The variational approach allows for a seamless combination of sparse Bayesian learning ideas and a variational Bayesian space-alternating generalized expectation-maximization (VB-SAGE) algorithm for estimating parameters of superimposed signals. While the former method realizes automatic regularization of ESNs, which also determines which echo states and input signals are relevant for "explaining" the desired signal, the latter method provides a basis for joint estimation of D&S readout parameters. The proposed training algorithm can naturally be extended to ESNs with fixed filter neurons. It also generalizes the recently proposed expectation-maximization-based D&S readout adaptation method. The proposed algorithm was tested on synthetic data prediction tasks as well as on dynamic handwritten character recognition.