Detailed insights into the formation pathway of CdS and ZnS in solution: a multi-modal in situ characterisation approach.

The high stability, high availability, and wide size-dependent bandgap energy of sulphidic semiconductor nanoparticles (NPs) render them promising for applications in optoelectronic devices and solar cells. However, the tunability of their optical properties depends on the strict control of their crystal structure and crystallisation process. Herein, we studied the structural evolution during the formation of CdS and ZnS in solution by combining in situ luminescence spectroscopy, synchrotron-based X-ray diffraction (XRD) and pair distribution function (PDF) analyses for the first time. The influence of precursor type, concentration, temperature and heating program on the product formation and on the bandgap or trap emission were investigated in detail. In summary, for CdS, single-source precursor (SSP) polyol strategies using the dichlorobis(thiourea)cadmium(II) complex and double-source precursor approaches combining Cd(CH3COO)2·2H2O and thiourea led to the straightforward product at 100 °C, while the catena((m2-acetato-O,O')-(acetate-O,O')-(m2-thiourea)-cadmium) complex was formed at 25 and 80 °C. For ZnS, the reaction between Zn(CH3COO)2·2H2O and thiourea at 100 °C led to the product formation after the crystallisation and dissolution of an unknown intermediate. At 180 °C, besides an unknown phase, the acetato-bis(thiourea)-zinc(II) complex was also detected as a reaction intermediate. The formation of such reaction intermediates, which generally remain undetected applying only ex situ characterisation approaches, reinforce the importance of in situ analysis for promoting the advance on the production of tailored semiconductor materials.

Hypothesis-driven modeling of the human lung-ventilator system: A characterization tool for Acute Respiratory Distress Syndrome research.

Mechanical ventilation is an essential tool in the management of Acute Respiratory Distress Syndrome (ARDS), but it exposes patients to the risk of ventilator-induced lung injury (VILI). The human lung-ventilator system (LVS) involves the interaction of complex anatomy with a mechanical apparatus, which limits the ability of process-based models to provide individualized clinical support. This work proposes a hypothesis-driven strategy for LVS modeling in which robust personalization is achieved using a pre-defined parameter basis in a non-physiological model. Model inversion, here via windowed data assimilation, forges observed waveforms into interpretable parameter values that characterize the data rather than quantifying physiological processes. Accurate, model-based inference on human-ventilator data indicates model flexibility and utility over a variety of breath types, including those from dyssynchronous LVSs. Estimated parameters generate static characterizations of the data that are 50%-70% more accurate than breath-wise single-compartment model estimates. They also retain sufficient information to distinguish between the types of breath they represent. However, the fidelity and interpretability of model characterizations are tied to parameter definitions and model resolution. These additional factors must be considered in conjunction with the objectives of specific applications, such as identifying and tracking the development of human VILI.

A methodology of phenotyping ICU patients from EHR data: High-fidelity, personalized, and interpretable phenotypes estimation.

OBJECTIVE: Computing phenotypes that provide high-fidelity, time-dependent characterizations and yield personalized interpretations is challenging, especially given the complexity of physiological and healthcare systems and clinical data quality. This paper develops a methodological pipeline to estimate unmeasured physiological parameters and produce high-fidelity, personalized phenotypes anchored to physiological mechanics from electronic health record (EHR). METHODS: A methodological phenotyping pipeline is developed that computes new phenotypes defined with unmeasurable computational biomarkers quantifying specific physiological properties in real time. Working within the inverse problem framework, this pipeline is applied to the glucose-insulin system for ICU patients using data assimilation to estimate an established mathematical physiological model with stochastic optimization. This produces physiological model parameter vectors of clinically unmeasured endocrine properties, here insulin secretion, clearance, and resistance, estimated for individual patient. These physiological parameter vectors are used as inputs to unsupervised machine learning methods to produce phenotypic labels and discrete physiological phenotypes. These phenotypes are inherently interpretable because they are based on parametric physiological descriptors. To establish potential clinical utility, the computed phenotypes are evaluated with external EHR data for consistency and reliability and with clinician face validation. RESULTS: The phenotype computation was performed on a cohort of 109 ICU patients who received no or short-acting insulin therapy, rendering continuous and discrete physiological phenotypes as specific computational biomarkers of unmeasured insulin secretion, clearance, and resistance on time windows of three days. Six, six, and five discrete phenotypes were found in the first, middle, and last three-day periods of ICU stays, respectively. Computed phenotypic labels were predictive with an average accuracy of 89%. External validation of discrete phenotypes showed coherence and consistency in clinically observable differences based on laboratory measurements and ICD 9/10 codes and clinical concordance from face validity. A particularly clinically impactful parameter, insulin secretion, had a concordance accuracy of 83%±27%. CONCLUSION: The new physiological phenotypes computed with individual patient ICU data and defined by estimates of mechanistic model parameters have high physiological fidelity, are continuous, time-specific, personalized, interpretable, and predictive. This methodology is generalizable to other clinical and physiological settings and opens the door for discovering deeper physiological information to personalize medical care.

From ligand exchange to reaction intermediates: what does really happen during the synthesis of emissive complexes?

In situ monitoring of the formation of emissive complexes is essential to enable the development of rational synthesis protocols, to provide accurate control over the generation of structure-related properties (such as luminescence) and to facilitate the development of new compounds. In situ luminescence analysis of coordination sensors (ILACS) utilizes the sensitivity of the spectroscopic properties of lanthanide ions to their coordination environment to detect structural changes during crystallization processes. Here, ILACS was utilized to monitor the formation of [Eu(bipy)2(NO3)3] (bipy = 2,2'-bipyridine) during co-precipitation synthesis. Validity of the ILACS results was ensured by concomitant utilization of in situ monitoring of other reaction parameters, including in situ measurements of pH value, ionic conductivity, and infrared spectra, as well as ex situ and synchrotron-based in situ X-ray diffraction analyses. Gradual desolvation of the Eu3+ ions and attachment of ligands were detected by an exponential increase of the intensity of the 5D0 → 7FJ (J = 0-4) transitions in the emission spectrum. Additionally, the in situ emission spectra show a decrease in the crystallization rate and an increase in the induction time in response to a reduction in the concentration of the starting solutions from 12 mM until crystallization ceased at starting reactant concentrations <6 mM. An increase to a three-fold higher concentration leads to the formation of a reaction intermediate, and its stability was determined to be highly concentration-dependent. The in situ luminescence measurements also demonstrated the existence of a ligand exchange process within the [Eu(bipy)2(NO3)3] complex upon addition of a phen (phen = 1,10'-phenanthroline) solution and the generation of a new phen-containing emissive complex. In attempting to solve the structure of this new phen-containing complex, a different, but nevertheless previously unsynthesized complex, [Eu(phen)2(NO3)3]bipy, was obtained, which shows characteristic Eu3+ luminescence in the red spectral range.

Intracranial pressure-flow relationships in traumatic brain injury patients expose gaps in the tenets of models and pressure-oriented management.

Background: The protocols and therapeutic guidance established for treating traumatic brain injuries (TBI) in neurointensive care focus on managing cerebral blood flow (CBF) and brain tissue oxygenation based on pressure signals. The decision support process relies on assumed relationships between cerebral perfusion pressure (CPP) and blood flow, pressure-flow relationships (PFRs), and shares this framework of assumptions with mathematical intracranial hemodynamic models. These foundational assumptions are difficult to verify, and their violation can impact clinical decision-making and model validity. Method: A hypothesis- and model-driven method for verifying and understanding the foundational intracranial hemodynamic PFRs is developed and applied to a novel multi-modality monitoring dataset. Results: Model analysis of joint observations of CPP and CBF validates the standard PFR when autoregulatory processes are impaired as well as unmodelable cases dominated by autoregulation. However, it also identifies a dynamical regime -or behavior pattern- where the PFR assumptions are wrong in a precise, data-inferable way due to negative CPP-CBF coordination over long timescales. This regime is of both clinical and research interest: its dynamics are modelable under modified assumptions while its causal direction and mechanistic pathway remain unclear. Conclusions: Motivated by the understanding of mathematical physiology, the validity of the standard PFR can be assessed a) directly by analyzing pressure reactivity and mean flow indices (PRx and Mx) or b) indirectly through the relationship between CBF and other clinical observables. This approach could potentially help personalize TBI care by considering intracranial pressure and CPP in relation to other data, particularly CBF. The analysis suggests a threshold using clinical indices of autoregulation jointly generalizes independently set indicators to assess CA functionality. These results support the use of increasingly data-rich environments to develop more robust hybrid physiological-machine learning models.

Identifying low-dimensional trajectories of mechanically-ventilated patient systems: Empirical phenotypes of joint patient+care processes to enhance temporal analysis in ARDS research.

Refined management of mechanically ventilation is an obvious target for improving patient outcomes, but is impeded by the nature of data for study and hypothesis generation. The connections between clinical outcomes and temporal development of iatrogenic injuries current lung-protective ventilator settings remain poorly understood. Analysis of lung-ventilator system (LVS) evolution at relevant timescales is frustrated by data volume and multiple sources of heterogeneity. This work motivates, presents, and validates a computational pipeline for resolving LVS systems into the joint evolution of data-conditioned model parameters and ventilator information. Applied to individuals, the workflow yields a concise low-dimensional representation of LVS behavior expressed in phenotypic breath waveforms suitable for analysis. The effectiveness of this approach is demonstrated through application to multi-day observational series of 35 patients. Individual patient analyses reveal multiple types of patient-oriented dynamics and breath behavior to expose the complexity of LVS evolution; less than 10% of phenotype changes related to ventilator settings changes. Dynamics are shown to including both stable and unstable phenotype transitions as well as both discrete and continuous changes unrelated to ventilator settings. At a cohort scale, 721 phenotypes constructed from individual data are condensed into a set of 16 groups that empirically organize around certain settings (positive end-expository pressure and ventilator mode) and structurally similar pressure-volume loop characterizations. Individual and cohort scale phenotypes, which may be refined by hypothesis-specific constructions, provide a common framework for ongoing temporal analysis and investigation of LVS dynamics.

A methodology of phenotyping ICU patients from EHR data: high-fidelity, personalized, and interpretable phenotypes estimation.

Objective: Computing phenotypes that provide high-fidelity, time-dependent characterizations and yield personalized interpretations is challenging, especially given the complexity of physiological and healthcare systems and clinical data quality. This paper develops a methodological pipeline to estimate unmeasured physiological parameters and produce high-fidelity, personalized phenotypes anchored to physiological mechanics from electronic health record (EHR). Methods: A methodological phenotyping pipeline is developed that computes new phenotypes defined with unmeasurable computational biomarkers quantifying specific physiological properties in real time. Working within the inverse problem framework, this pipeline is applied to the glucose-insulin system for ICU patients using data assimilation to estimate an established mathematical physiological model with stochastic optimization. This produces physiological model parameter vectors of clinically unmeasured endocrine properties, here insulin secretion, clearance, and resistance, estimated for individual patient. These physiological parameter vectors are used as inputs to unsupervised machine learning methods to produce phenotypic labels and discrete physiological phenotypes. These phenotypes are inherently interpretable because they are based on parametric physiological descriptors. To establish potential clinical utility, the computed phenotypes are evaluated with external EHR data for consistency and reliability and with clinician face validation. Results: The phenotype computation was performed on a cohort of 109 ICU patients who received no or short-acting insulin therapy, rendering continuous and discrete physiological phenotypes as specific computational biomarkers of unmeasured insulin secretion, clearance, and resistance on time windows of three days. Six, six, and five discrete phenotypes were found in the first, middle, and last three-day periods of ICU stays, respectively. Computed phenotypic labels were predictive with an average accuracy of 89%. External validation of discrete phenotypes showed coherence and consistency in clinically observable differences based on laboratory measurements and ICD 9/10 codes and clinical concordance from face validity. A particularly clinically impactful parameter, insulin secretion, had a concordance accuracy of 83% ± 27%. Conclusion: The new physiological phenotypes computed with individual patient ICU data and defined by estimates of mechanistic model parameters have high physiological fidelity, are continuous, time-specific, personalized, interpretable, and predictive. This methodology is generalizable to other clinical and physiological settings and opens the door for discovering deeper physiological information to personalize medical care.

Clinical Decision Support for Traumatic Brain Injury: Identifying a Framework for Practical Model-Based Intracranial Pressure Estimation at Multihour Timescales.

BACKGROUND: The clinical mitigation of intracranial hypertension due to traumatic brain injury requires timely knowledge of intracranial pressure to avoid secondary injury or death. Noninvasive intracranial pressure (nICP) estimation that operates sufficiently fast at multihour timescales and requires only common patient measurements is a desirable tool for clinical decision support and improving traumatic brain injury patient outcomes. However, existing model-based nICP estimation methods may be too slow or require data that are not easily obtained. OBJECTIVE: This work considers short- and real-time nICP estimation at multihour timescales based on arterial blood pressure (ABP) to better inform the ongoing development of practical models with commonly available data. METHODS: We assess and analyze the effects of two distinct pathways of model development, either by increasing physiological integration using a simple pressure estimation model, or by increasing physiological fidelity using a more complex model. Comparison of the model approaches is performed using a set of quantitative model validation criteria over hour-scale times applied to model nICP estimates in relation to observed ICP. RESULTS: The simple fully coupled estimation scheme based on windowed regression outperforms a more complex nICP model with prescribed intracranial inflow when pulsatile ABP inflow conditions are provided. We also show that the simple estimation data requirements can be reduced to 1-minute averaged ABP summary data under generic waveform representation. CONCLUSIONS: Stronger performance of the simple bidirectional model indicates that feedback between the systemic vascular network and nICP estimation scheme is crucial for modeling over long intervals. However, simple model reduction to ABP-only dependence limits its utility in cases involving other brain injuries such as ischemic stroke and subarachnoid hemorrhage. Additional methodologies and considerations needed to overcome these limitations are illustrated and discussed.

Real-time electronic health record mortality prediction during the COVID-19 pandemic: a prospective cohort study.

OBJECTIVE: To rapidly develop, validate, and implement a novel real-time mortality score for the COVID-19 pandemic that improves upon sequential organ failure assessment (SOFA) for decision support for a Crisis Standards of Care team. MATERIALS AND METHODS: We developed, verified, and deployed a stacked generalization model to predict mortality using data available in the electronic health record (EHR) by combining 5 previously validated scores and additional novel variables reported to be associated with COVID-19-specific mortality. We verified the model with prospectively collected data from 12 hospitals in Colorado between March 2020 and July 2020. We compared the area under the receiver operator curve (AUROC) for the new model to the SOFA score and the Charlson Comorbidity Index. RESULTS: The prospective cohort included 27 296 encounters, of which 1358 (5.0%) were positive for SARS-CoV-2, 4494 (16.5%) required intensive care unit care, 1480 (5.4%) required mechanical ventilation, and 717 (2.6%) ended in death. The Charlson Comorbidity Index and SOFA scores predicted mortality with an AUROC of 0.72 and 0.90, respectively. Our novel score predicted mortality with AUROC 0.94. In the subset of patients with COVID-19, the stacked model predicted mortality with AUROC 0.90, whereas SOFA had AUROC of 0.85. DISCUSSION: Stacked regression allows a flexible, updatable, live-implementable, ethically defensible predictive analytics tool for decision support that begins with validated models and includes only novel information that improves prediction. CONCLUSION: We developed and validated an accurate in-hospital mortality prediction score in a live EHR for automatic and continuous calculation using a novel model that improved upon SOFA.

Real-Time Electronic Health Record Mortality Prediction During the COVID-19 Pandemic: A Prospective Cohort Study.

BACKGROUND: The SARS-CoV-2 virus has infected millions of people, overwhelming critical care resources in some regions. Many plans for rationing critical care resources during crises are based on the Sequential Organ Failure Assessment (SOFA) score. The COVID-19 pandemic created an emergent need to develop and validate a novel electronic health record (EHR)-computable tool to predict mortality. RESEARCH QUESTIONS: To rapidly develop, validate, and implement a novel real-time mortality score for the COVID-19 pandemic that improves upon SOFA. STUDY DESIGN AND METHODS: We conducted a prospective cohort study of a regional health system with 12 hospitals in Colorado between March 2020 and July 2020. All patients >14 years old hospitalized during the study period without a do not resuscitate order were included. Patients were stratified by the diagnosis of COVID-19. From this cohort, we developed and validated a model using stacked generalization to predict mortality using data widely available in the EHR by combining five previously validated scores and additional novel variables reported to be associated with COVID-19-specific mortality. We compared the area under the receiver operator curve (AUROC) for the new model to the SOFA score and the Charlson Comorbidity Index. RESULTS: We prospectively analyzed 27,296 encounters, of which 1,358 (5.0%) were positive for SARS-CoV-2, 4,494 (16.5%) included intensive care unit (ICU)-level care, 1,480 (5.4%) included invasive mechanical ventilation, and 717 (2.6%) ended in death. The Charlson Comorbidity Index and SOFA scores predicted overall mortality with an AUROC of 0.72 and 0.90, respectively. Our novel score predicted overall mortality with AUROC 0.94. In the subset of patients with COVID-19, we predicted mortality with AUROC 0.90, whereas SOFA had AUROC of 0.85. INTERPRETATION: We developed and validated an accurate, in-hospital mortality prediction score in a live EHR for automatic and continuous calculation using a novel model, that improved upon SOFA. TAKE HOME POINTS: Study Question: Can we improve upon the SOFA score for real-time mortality prediction during the COVID-19 pandemic by leveraging electronic health record (EHR) data?Results: We rapidly developed and implemented a novel yet SOFA-anchored mortality model across 12 hospitals and conducted a prospective cohort study of 27,296 adult hospitalizations, 1,358 (5.0%) of which were positive for SARS-CoV-2. The Charlson Comorbidity Index and SOFA scores predicted all-cause mortality with AUROCs of 0.72 and 0.90, respectively. Our novel score predicted mortality with AUROC 0.94.Interpretation: A novel EHR-based mortality score can be rapidly implemented to better predict patient outcomes during an evolving pandemic.

Prognostic factors in acute pulmonary edema.

Over a six-month period, 106 admissions of 94 patients for acute pulmonary edema were identified and their charts were reviewed. Precipitating factors for acute pulmonary edema included progressively worsening congestive heart failure in 25.5% of cases, coronary insufficiency in 20.8%, subendocardial myocardial infarction in 16.0%, acute transmural myocardial infarction in 10.4%, arrhythmia in 8.5%, medical noncompliance in 6.6%, and other causes in 12.6%. In-hospital mortality was 17.0% (16 patients). Of those patients discharged from the hospital, an additional 27 (39.7%) were dead at one year, giving an overall one-year mortality of 51.2%. We found that patients with progressively worsening congestive heart failure have a better prognosis than patients with other precipitants. Also, patients with an initial systolic blood pressure in the emergency room of 160 mm Hg or higher had an improved survival over patients with an initial systolic blood pressure under 160 mm Hg. No other in-hospital or long-term prognostic factors were identified.

Twice-daily administration of oral verapamil in the treatment of essential hypertension.

The antihypertensive effect of twice-daily administration of verapamil hydrochloride was evaluated in 21 adult patients with mild to moderate essential hypertension. Following four weeks of placebo therapy, verapamil was given for four weeks with a treatment goal of sitting diastolic blood pressure (BP) of less than 90 mm Hg, or to a maximum dose of 160 mg twice daily. Sitting and standing BPs, heart rate, and verapamil plasma levels were determined weekly, ten to 12 hours post dose. At the maximal dose (mean, 154 +/- 19.2 mg), heart rate was not affected, side effects were minimal, and sitting diastolic BP was significantly reduced from placebo baseline, with 12 of 21 patients having a fall in sitting diastolic BP of 10 mm Hg or more or less than 90 mm Hg. A trough verapamil plasma level of greater than 80 ng/mL was associated with a good hypotensive response. These data indicate the safety and utility of twice-daily verapamil administration for the treatment of essential hypertension and suggest the value of obtaining verapamil plasma levels as a guide to dosage determination.

Characterization of glycosylated and catalytically active recombinant human alpha-galactosidase A using a baculovirus vector.

Fabry disease is an X-linked inborn error of glycolipid metabolism caused by a deficiency of the lysosomal enzyme alpha-galactosidase A (GalA; EC 3.2.1.22). In order to obtain large quantities of this human enzyme for physical characterization and for the development of new approaches for enzyme therapy, we constructed derivatives of the Autographa californica nuclear polyhedrosis virus that produce the human enzyme. The recombinant GalA (re-GalA) is produced at high levels, and is active with both the artificial substrate, 4-methylumbelliferyl-alpha-D-galactopyranoside, and the natural in vivo substrate, trihexosylceramide. The purified re-GalA is glycosylated and is taken up by normal and Fabry fibroblasts in cell culture. Mass spectral analysis of total monosaccharides released by hydrazinolysis indicates that it contains fucose, galactose, mannose and N-acetylglucosamine. Amino-acid sequence analysis of six proteolytic peptides corresponded to sequences predicted by the cDNA. The molecular masses of the purified enzyme, estimated by electrospray mass spectroscopy and laser desorption time-of-flight analysis are 46.85 and 46.62 kDa, respectively, approx. 10% greater than the polypeptide portion predicted by the cDNA. The recombinant enzyme retains significant catalytic activity after modification with poly(ethylene glycol), a treatment which decreases the immunogenicity and increases the circulation life of many proteins used therapeutically.

Comparison of diltiazem and nifedipine for both angina pectoris and systemic hypertension.

In a randomized, double-blind, placebo-controlled crossover trial, diltiazem and nifedipine were compared in 10 patients with stable angina pectoris and mild to moderate hypertension (supine diastolic blood pressure greater than or equal to 90 mm Hg). Patients received placebo for 2 weeks, then increasing doses of diltiazem (90 to 360 mg/day) or nifedipine (30 to 120 mg/day) in 3 daily divided doses over 2 weeks, followed by 1 week of therapy at the maximal dose, a 1-week placebo "washout," then crossover to the other drug. Heart rate and blood pressure at rest and during exercise, anginal frequency, nitroglycerin consumption and treadmill exercise tolerance were assessed. Compared with placebo, anginal frequency and nitroglycerin consumption were reduced with both diltiazem and nifedipine (p less than 0.01) and exercise tolerance was increased with both drugs (p less than 0.01). Standing blood pressure at rest was reduced by diltiazem and nifedipine (146.6 +/- 11.4/97.7 +/- 5.3 mm Hg at placebo, baseline reduced to 129.6 +/- 15.2/79.5 +/- 13.7 mm Hg with diltiazem, and to 122.2 +/- 9.9/82.0 +/- 7.1 with nifedipine, p less than 0.01 for both). Compared with placebo, diltiazem and nifedipine also reduced exercise diastolic blood pressure (p less than 0.01), but not systolic blood pressure. Diltiazem lowered the heart rate at rest from 88.5 +/- 14.4 beats/min at placebo baseline to 79.7 +/- 17.9 beats/min (p less than 0.01); the heart rate with diltiazem was 11 beats/min lower than that with nifedipine (p less than 0.05). Both diltiazem and nifedipine had similar effects on the heart rate-blood pressure product at rest and during exercise.(ABSTRACT TRUNCATED AT 250 WORDS)

Acute monoblastic leukemia (FAB-M5b) with t(8;14)(p11;q11.1).

A case of acute monocytic leukemia (FAB-M5b) expressing natural killer cell-associated antigens containing a t(8;14)(p11;q11.1) is presented. We interpret this translocation to represent a variant of the t(8;16) previously reported in FAB-M5b. These findings support the contention that the 8p11 breakpoint site is the critical junction in the oncogenesis of acute monoblastic leukemia with differentiation.

Doppler-derived aortic flow measurements during and after heavy isometric exercise in healthy men versus men with myocardial infarction.

Doppler echocardiography is a useful noninvasive determination of left ventricular function during dynamic exercise. Scarce data are available for the use of this technique during heavy isometric exercise. Therefore, Doppler-derived aortic flow indexes were assessed during and after 50% maximal upper-body isometric exercise in 25 healthy men (aged 47 +/- 6 years) and compared with those of 22 men (aged 48 +/- 9 years) who had suffered myocardial infarction. The heart rate increased (p = 0.01) in each of the groups from a mean of 68 +/- 12 at rest to 84 +/- 11 during isometric exercise. At rest, systolic blood pressure was higher (p = 0.05) in the patients with coronary artery disease. During exercise, the patients with cardiac disease, compared with the healthy volunteers, demonstrated a lesser reduction in flow velocity integral, stroke volume, and cardiac indexes (p = 0.001). Immediately on recovery, the patients with cardiac disease, compared with the healthy group, showed significantly greater (p = 0.001) increase in stroke volume and cardiac indexes. At 3 minute's recovery, the stroke volume index continued to increase in the patients with cardiac disease, while the healthy group showed a decrease to below its resting value. Although 50% of maximal upper-body isometric exercise caused similar heart rate and systolic blood pressure responses in healthy patients and patients with cardiac disease, there were significant group differences in Doppler-derived left ventricular systolic function indexes, which were greatest on immediate and 3 minute's recovery. The results suggest that this novel isometric test may be useful in clinical testing.

Isolated systolic hypertension in the elderly: pathophysiology and treatment.

Isolated systolic hypertension (ISH) is a common clinical finding in the elderly population and appears to be a risk factor for cardiovascular morbidity and mortality. It appears feasible and safe to treat patients with various antihypertensive drugs; however, the morbidity and mortality benefits still need to be determined.

Calcium channel blockers in systemic hypertension.

Alterations in transmembrane flux of calcium ions may be playing a role in the pathophysiology of systemic hypertension. Calcium channel blockers have been shown to be effective antihypertensive drugs with excellent safety profiles. They are efficacious in the long term treatment of systemic hypertension in all population subgroups, and have special applicability for treating patients with hypertensive urgencies and individuals with concomitant diseases such as angina pectoris and arrhythmias.

Use of capillary electrophoresis-electrospray ionization mass spectrometry in the analysis of synthetic peptides.

We have constructed a capillary electrophoresis (CE) system with UV detection and have successfully interfaced it to an electrospray ionization mass spectrometry (ES-MS) system. A synthesized fragment of heregulin-beta (212-226) was thought to be a single component by re-injection into an HPLC system, but results from CE-UV-ES-MS indicated that a dehydration product was present in the desired peptide sample. A synthetic heregulin-alpha (177-241) was isolated by preparative HPLC, but re-injection on an analytical system indicated a tailing peak. CE-UV-ES-MS indicated a mixture whose two major components were of the same nominal molecular mass (within experimental error), suggesting the presence of an isomer or a deamidation product. The results show that CE-UV-ES-MS can be used as an orthogonal analytical technique to solve practical problems encountered in peptide synthesis laboratories.