The phenomenon of fast-growing companies exhibiting sustained growth and creating disproportionally many new jobs, so-called “gazelles”, has been widely analyzed in the literature. The criteria defining “gazelles”, however, lack a consensus, while it cannot be ruled out that superior performance of these companies is just good luck. We use large firm-level datasets for Russia and Spain and conduct a Monte Carlo experiment with first-order Markov chains to derive a definition of “gazelle” companies and ensure that their existence cannot be explained by chance only. Our results demonstrate that the definitions of “gazelle” companies differ between the two countries warning against using same definition for different countries. We find that the “gazelles” account for about 1–2% of the companies in our datasets and are responsible for approximately 14% of employment growth in Russia and 9% in Spain. These companies are concentrated in economic sectors like retail trade, real estate and construction.
Patient: Female, 51-year-old Final Diagnosis: Multiple myeloma with extramedullary plasmacytoma of parotid gland Symptoms: Headache • weight loss • vision change • facial mass Clinical Procedure: Incisional biopsy Specialty: Head & Neck Surgery • Infectious Disease • Internal Medicine Objective: Challenging differential diagnosis Background: The differential diagnosis for a parotid mass is broad, including infectious, autoimmune, and neoplastic etiologies. In people with HIV, regardless of viral suppression or immune status, neoplastic causes are more common. This report describes the evaluation of a woman with a large parotid mass, with an ultimate diagnosis of multiple myeloma with extramedullary plasmacytoma. Case Report: A 51-year-old woman with HIV infection presented with headache, weight loss, and right facial mass that was present for 5 years but more rapidly enlarging in the prior year. CD4 count was 234 cells/mL, and HIV RNA was 10 810 copies/mL. Physical examination was significant for a large deforming right-sided facial mass, decreased sensation in the V1 and V2 distributions, and right-sided ophthalmoplegia and ptosis. MRI and PET/CT scan confirmed a metabolically active large parotid mass with extension into the cavernous sinus. An IgG kappa monoclonal spike was present on serum protein electrophoresis. Incisional biopsy of the facial mass showed atypical lymphoid cells with plasmablastic and plasmacytic morphology with a high mitotic rate and proliferation index. She was diagnosed with R-ISS stage II IgG kappa multiple myeloma with extramedullary plasmacytoma, and initiated on chemotherapy, radiation, and antiretroviral therapy. Conclusions: A rapidly enlarging parotid mass should prompt timely evaluation and biopsy for definitive diagnosis, particularly in immunocompromised patients, including people with HIV. Extramedullary plasmacytomas have a more aggressive disease process in people with HIV and are associated with high-risk multiple myeloma and progression, as seen in this patient.
Chronic stroke patients may have lower resting metabolic rate (RMR) due to disability and ensuing loss of skeletal muscle. Established equations used to estimate RMR based on weight, height, age, or lean body mass in healthy non-stroke individuals may not be accurate for a hemiparetic patient population. The purpose of this study is to determine resting metabolic rate in chronic stroke survivors and compare to RMR calculated with established equations in healthy adults. Adults (n=71; 56 males, 15 females; 40 African American, 27 Caucasian, 4 other / not reported) aged 44-76 years (61 ± 7.5 yrs) who had a history of chronic stroke (> 3 months prior) underwent a 30 minute test after a 12-hour fast to measure RMR by indirect calorimetry, total body DXA scan, and treadmill test (VO2 peak). Estimated RMR was calculated using nine established equations. RMR measured in the total group (1552 ± 319 kcal/day) was significantly different from all nine estimated RMR values (Katch-McArdle 1664 ± 242 kcal/day, P=0.05; Livingston 1671 ± 239 kcal/day, P<0.001; Mifflin 1703 ± 254 kcal/day, P<0.001; Owen 1761 ± 269 kcal/day, P<0.001; Harris Benedict 1782 ± 308 kcal/day, P<0.001; revised Harris-Benedict 1795 ± 306 kcal/day, P<0.001; Cunningham 1818 ± 247 kcal/day, P<0.001; Schofield 2147 ± 301 kcal/day, P<0.001; IMNA 2428 ± 405 kcal/day, P<0.001). Calculated RMR was between 9% and 60% greater than measured RMR, regardless of race. Appendicular lean mass (r=0.65, P<0.001), total lean mass (r=0.65, P<0.001), and VO2 peak (r=0.50, P<0.001) were associated with measured RMR. RMR estimation equations established in healthy adults are not reliable for the chronic stroke population, indicating the need for a more accurate predictive equation to better assist nutritional status in patients with conditions of muscle atrophy.
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