Moving towards a control technique to help small firms monitor and control key marketing parameters: a survival aid

This article considers that one way to help the small- and medium-sized enterprise (SME) to survive is to offer it a robust but simple monitoring and control technique that would help it manage the business effectively and this, in turn, should help to increase its chances of survival. This technique should also be of interest to all people involved with monitoring or advising a large number of small enterprises or business units within a larger organization. For example, a bank manager or a small business consultant responsible for a portfolio of firms. The authors utilize process control techniques more often used in production and inventory control systems to demonstrate how one might monitor the marketing ``health'' of small firms

Non Inflammatory Boronate Based Glucose-Responsive Insulin Delivery Systems

Boronic acids, known to bind diols, were screened to identify non-inflammatory cross-linkers for the preparation of glucose sensitive and insulin releasing agglomerates of liposomes (Agglomerated Vesicle Technology-AVT). This was done in order to select a suitable replacement for the previously used cross-linker, ConcanavalinA (ConA), a lectin known to have both toxic and inflammatory effects in vivo. Lead-compounds were selected from screens that involved testing for inflammatory potential, cytotoxicity and glucose-binding. These were then conjugated to insulin-encapsulating nanoparticles and agglomerated via sugar-boronate ester linkages to form AVTs. In vitro, the particles demonstrated triggered release of insulin upon exposure to physiologically relevant concentrations of glucose (10 mmoles/L–40 mmoles/L). The agglomerates were also shown to be responsive to multiple spikes in glucose levels over several hours, releasing insulin at a rate defined by the concentration of the glucose trigger

New Dual Mode Gadolinium Nanoparticle Contrast Agent for Magnetic Resonance Imaging

BACKGROUND: Liposomal-based gadolinium (Gd) nanoparticles have elicited significant interest for use as blood pool and molecular magnetic resonance imaging (MRI) contrast agents. Previous generations of liposomal MR agents contained gadolinium-chelates either within the interior of liposomes (core-encapsulated gadolinium liposomes) or presented on the surface of liposomes (surface-conjugated gadolinium liposomes). We hypothesized that a liposomal agent that contained both core-encapsulated gadolinium and surface-conjugated gadolinium, defined herein as dual-mode gadolinium (Dual-Gd) liposomes, would result in a significant improvement in nanoparticle-based T1 relaxivity over the previous generations of liposomal agents. In this study, we have developed and tested, both in vitro and in vivo, such a dual-mode liposomal-based gadolinium contrast agent. METHODOLOGY/PRINCIPAL FINDINGS: THREE TYPES OF LIPOSOMAL AGENTS WERE FABRICATED: core-encapsulated, surface-conjugated and dual-mode gadolinium liposomes. In vitro physico-chemical characterizations of the agents were performed to determine particle size and elemental composition. Gadolinium-based and nanoparticle-based T1 relaxivities of various agents were determined in bovine plasma. Subsequently, the agents were tested in vivo for contrast-enhanced magnetic resonance angiography (CE-MRA) studies. Characterization of the agents demonstrated the highest gadolinium atoms per nanoparticle for Dual-Gd liposomes. In vitro, surface-conjugated gadolinium liposomes demonstrated the highest T1 relaxivity on a gadolinium-basis. However, Dual-Gd liposomes demonstrated the highest T1 relaxivity on a nanoparticle-basis. In vivo, Dual-Gd liposomes resulted in the highest signal-to-noise ratio (SNR) and contrast-to-noise ratio in CE-MRA studies. CONCLUSIONS/SIGNIFICANCE: The dual-mode gadolinium liposomal contrast agent demonstrated higher particle-based T1 relaxivity, both in vitro and in vivo, compared to either the core-encapsulated or the surface-conjugated liposomal agent. The dual-mode gadolinium liposomes could enable reduced particle dose for use in CE-MRA and increased contrast sensitivity for use in molecular imaging

Computed Tomography Imaging of Primary Lung Cancer in Mice Using a Liposomal-Iodinated Contrast Agent

To investigate the utility of a liposomal-iodinated nanoparticle contrast agent and computed tomography (CT) imaging for characterization of primary nodules in genetically engineered mouse models of non-small cell lung cancer.Primary lung cancers with mutations in K-ras alone (Kras(LA1)) or in combination with p53 (LSL-Kras(G12D);p53(FL/FL)) were generated. A liposomal-iodine contrast agent containing 120 mg Iodine/mL was administered systemically at a dose of 16 µl/gm body weight. Longitudinal micro-CT imaging with cardio-respiratory gating was performed pre-contrast and at 0 hr, day 3, and day 7 post-contrast administration. CT-derived nodule sizes were used to assess tumor growth. Signal attenuation was measured in individual nodules to study dynamic enhancement of lung nodules.A good correlation was seen between volume and diameter-based assessment of nodules (R(2)>0.8) for both lung cancer models. The LSL-Kras(G12D);p53(FL/FL) model showed rapid growth as demonstrated by systemically higher volume changes compared to the lung nodules in Kras(LA1) mice (p<0.05). Early phase imaging using the nanoparticle contrast agent enabled visualization of nodule blood supply. Delayed-phase imaging demonstrated significant differential signal enhancement in the lung nodules of LSL-Kras(G12D);p53(FL/FL) mice compared to nodules in Kras(LA1) mice (p<0.05) indicating higher uptake and accumulation of the nanoparticle contrast agent in rapidly growing nodules.The nanoparticle iodinated contrast agent enabled visualization of blood supply to the nodules during the early-phase imaging. Delayed-phase imaging enabled characterization of slow growing and rapidly growing nodules based on signal enhancement. The use of this agent could facilitate early detection and diagnosis of pulmonary lesions as well as have implications on treatment response and monitoring

A natural language processing and deep learning approach to identify child abuse from pediatric electronic medical records.

Child physical abuse is a leading cause of traumatic injury and death in children. In 2017, child abuse was responsible for 1688 fatalities in the United States, of 3.5 million children referred to Child Protection Services and 674,000 substantiated victims. While large referral hospitals maintain teams trained in Child Abuse Pediatrics, smaller community hospitals often do not have such dedicated resources to evaluate patients for potential abuse. Moreover, identification of abuse has a low margin of error, as false positive identifications lead to unwarranted separations, while false negatives allow dangerous situations to continue. This context makes the consistent detection of and response to abuse difficult, particularly given subtle signs in young, non-verbal patients. Here, we describe the development of artificial intelligence algorithms that use unstructured free-text in the electronic medical record-including notes from physicians, nurses, and social workers-to identify children who are suspected victims of physical abuse. Importantly, only the notes from time of first encounter (e.g.: birth, routine visit, sickness) to the last record before child protection team involvement were used. This allowed us to develop an algorithm using only information available prior to referral to the specialized child protection team. The study was performed in a multi-center referral pediatric hospital on patients screened for abuse within five different locations between 2015 and 2019. Of 1123 patients, 867 records were available after data cleaning and processing, and 55% were abuse-positive as determined by a multi-disciplinary team of clinical professionals. These electronic medical records were encoded with three natural language processing (NLP) algorithms-Bag of Words (BOW), Word Embeddings (WE), and Rules-Based (RB)-and used to train multiple neural network architectures. The BOW and WE encodings utilize the full free-text, while RB selects crucial phrases as identified by physicians. The best architecture was selected by average classification accuracy for the best performing model from each train-test split of a cross-validation experiment. Natural language processing coupled with neural networks detected cases of likely child abuse using only information available to clinicians prior to child protection team referral with average accuracy of 0.90±0.02 and average area under the receiver operator characteristic curve (ROC-AUC) 0.93±0.02 for the best performing Bag of Words models. The best performing rules-based models achieved average accuracy of 0.77±0.04 and average ROC-AUC 0.81±0.05, while a Word Embeddings strategy was severely limited by lack of representative embeddings. Importantly, the best performing model had a false positive rate of 8%, as compared to rates of 20% or higher in previously reported studies. This artificial intelligence approach can help screen patients for whom an abuse concern exists and streamline the identification of patients who may benefit from referral to a child protection team. Furthermore, this approach could be applied to develop computer-aided-diagnosis platforms for the challenging and often intractable problem of reliably identifying pediatric patients suffering from physical abuse

Determination of Electrical Contact Resistivity in Thermoelectric Modules (TEMS) from Module-Level Measurements

An experimental apparatus was developed to characterize the performance of a thermoelectric module (TEM) and heat sink assembly when the TEM was operated in refrigeration mode. A numerical model was developed to simulate the experiments. Bulk and interfacial Ohmic heating, the Peltier effect, Thomson effect and temperature-dependent bulk material properties, i.e., Seebeck coefficient and electrical conductivity were considered. A novel, self-consistent characterization methodology was developed to obtain the electrical contact resistivity at the interconnects in a TEM from the numerical simulations and the experiments. The electrical contact resistivity of the module tested was determined to be approximately 1.0 × 10−9 omega_m2. The predictions are consistent with electrical contact resistivity obtained based on the performance specifications (Tmax) of the TEM